{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a2f143c7",
   "metadata": {},
   "source": [
    "# 神经网络编程练习中\n",
    "\n",
    "本章我们会逐步完成：\n",
    "* 编码实现Dropout传播；\n",
    "* 编码组合Affine-ReLU-Dropout层；\n",
    "* 编码实现Dropout神经网络；\n",
    "* 解耦神经网络；\n",
    "* 正则化比较实验。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8ac29747",
   "metadata": {},
   "outputs": [],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "import time\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from classifiers.chapter4 import *\n",
    "from utils import *\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) \n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "def rel_error(x, y):\n",
    "    \"\"\" 返回相对误差 \"\"\"\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "29433c51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_train:  (49000, 3, 32, 32)\n",
      "y_train:  (49000,)\n",
      "X_val:  (1000, 3, 32, 32)\n",
      "y_val:  (1000,)\n",
      "X_test:  (1000, 3, 32, 32)\n",
      "y_test:  (1000,)\n"
     ]
    }
   ],
   "source": [
    "# 载入预处理后的数据\n",
    "data = get_CIFAR10_data()\n",
    "for k, v in data.items():\n",
    "    print('%s: ' % k, v.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b9a5dfa",
   "metadata": {},
   "source": [
    "## Dropout 前向传播\n",
    "\n",
    "由于Dropout的训练阶段和测试阶段采取不一样的传播方式，因此我们会设置“test”以及“train”模式。在训练模式时，我们会将单独的神经元激活概率P生成mask掩码层。该掩码层中为“0”的位置表明该位置处神经元处于抑制状态，为“1”的位置表明该处神经元可用。在测试阶段，我们去除掩码操作，直接返回输入结果即可。\n",
    "\n",
    "执行下面的代码，进行检验。测试概率p与训练阶段输出0的平均个数相加与1接近。\n",
    "\n",
    "Dropout使一部分神经元失活，那么该层神经元的输出均值就会缩小p倍。这在零均值的时候没有影响，但如果我们的神经元不是零均值分布的，那么可能就会收到影响。为了消除这种影响，我们使用mask=mask/p，这样就可以保证输出均值与输入均值相同，并且也间接放大了可用神经元的“影响力”。而现在神经元处于激活状态时，其输出值会被方法1/p倍。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "527b34ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "测试概率 p =  0.3\n",
      "均值输入:  9.998715427214176\n",
      "训练阶段输出均值:  10.065172915888757\n",
      "测试阶段输出均值:  9.998715427214176\n",
      "训练阶段输出为0的平均个数:  0.69798\n",
      "测试阶段输出为0的平均个数:  0.0\n",
      "测试概率 p =  0.6\n",
      "均值输入:  9.998715427214176\n",
      "训练阶段输出均值:  9.982967021634078\n",
      "测试阶段输出均值:  9.998715427214176\n",
      "训练阶段输出为0的平均个数:  0.400804\n",
      "测试阶段输出为0的平均个数:  0.0\n",
      "测试概率 p =  0.75\n",
      "均值输入:  9.998715427214176\n",
      "训练阶段输出均值:  9.998538139301417\n",
      "测试阶段输出均值:  9.998715427214176\n",
      "训练阶段输出为0的平均个数:  0.250004\n",
      "测试阶段输出为0的平均个数:  0.0\n"
     ]
    }
   ],
   "source": [
    "from classifiers.chapter4.dropout_layers import *\n",
    "\n",
    "x = np.random.randn(500, 500) + 10\n",
    "\n",
    "for p in [0.3, 0.6, 0.75]:\n",
    "    out, _ = dropout_forward(x, {'mode': 'train', 'p': p})\n",
    "    out_test, _ = dropout_forward(x, {'mode': 'test', 'p': p})\n",
    "\n",
    "    print('测试概率 p = ', p)\n",
    "    print('均值输入: ', x.mean())\n",
    "    print('训练阶段输出均值: ', out.mean())\n",
    "    print('测试阶段输出均值: ', out_test.mean())\n",
    "    print('训练阶段输出为0的平均个数: ',(out == 0).mean())\n",
    "    print('测试阶段输出为0的平均个数: ',(out_test == 0).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40687c89",
   "metadata": {},
   "source": [
    "## Dropout 反向传播\n",
    "\n",
    "反向传播同样分为测试和训练两种模式。在测试阶段，仅仅返回上层梯度即可。在训练阶段需要将处于抑制状态神经元所对应的上层梯度设置为0。因此需要使用mask，其已经放在cache中。\n",
    "\n",
    "当你完成编码工作后，执行下列代码块进行检验："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d3a215a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dx 相对误差:  5.4456084067211455e-11\n"
     ]
    }
   ],
   "source": [
    "from utils import *\n",
    "\n",
    "x = np.random.randn(10, 10) + 10\n",
    "dout = np.random.randn( * x.shape )\n",
    "\n",
    "dropout_param = { 'mode': 'train', 'p': 0.8, 'seed': 123 }\n",
    "out, cache = dropout_forward( x, dropout_param )\n",
    "dx = dropout_backward( dout, cache )\n",
    "# 计算梯度\n",
    "dx_num = eval_numerical_gradient_array( lambda xx: \n",
    "                                       dropout_forward( xx, dropout_param)[0], x, dout)\n",
    "\n",
    "print('dx 相对误差: ', rel_error( dx, dx_num ))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f78fd16",
   "metadata": {},
   "source": [
    "## Dropout全连接神经网络\n",
    "\n",
    "接下来,我们将Dropout功能添加到深层全连接神经网络中。主要完成Dropout神经网络初始化以及损失函数的构建。\n",
    "\n",
    "然后进行梯度校验。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "18d393b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "检验 dropout率 =  0\n",
      "初始化 loss:  2.3051948273987857\n",
      "W1 相对误差: 2.53e-07\n",
      "W2 相对误差: 1.50e-05\n",
      "W3 相对误差: 2.75e-07\n",
      "b1 相对误差: 2.94e-06\n",
      "b2 相对误差: 5.05e-08\n",
      "b3 相对误差: 1.17e-10\n",
      "\n",
      "检验 dropout率 =  0.2\n",
      "初始化 loss:  2.2821321641253425\n",
      "W1 相对误差: 7.28e-09\n",
      "W2 相对误差: 8.40e-10\n",
      "W3 相对误差: 5.63e-09\n",
      "b1 相对误差: 1.64e-09\n",
      "b2 相对误差: 2.21e-10\n",
      "b3 相对误差: 1.48e-10\n",
      "\n",
      "检验 dropout率 =  0.5\n",
      "初始化 loss:  2.3011143171656236\n",
      "W1 相对误差: 1.89e-07\n",
      "W2 相对误差: 1.94e-06\n",
      "W3 相对误差: 2.04e-08\n",
      "b1 相对误差: 5.02e-08\n",
      "b2 相对误差: 2.45e-09\n",
      "b3 相对误差: 1.66e-10\n",
      "\n",
      "检验 dropout率 =  0.7\n",
      "初始化 loss:  2.3040345536668467\n",
      "W1 相对误差: 1.77e-07\n",
      "W2 相对误差: 7.78e-07\n",
      "W3 相对误差: 3.39e-08\n",
      "b1 相对误差: 1.77e-08\n",
      "b2 相对误差: 3.20e-09\n",
      "b3 相对误差: 8.99e-11\n",
      "\n"
     ]
    }
   ],
   "source": [
    "N, D, H1, H2, C = 2, 15, 20, 30, 10\n",
    "X = np.random.randn(N, D)\n",
    "y = np.random.randint(C, size=(N,))\n",
    "\n",
    "for dropout in [0, 0.2, 0.5,0.7]:\n",
    "    print('检验 dropout率 = ', dropout)\n",
    "    model = FullyConnectedNet(input_dim=D,hidden_dims=[H1, H2],    num_classes=C,\n",
    "                                                        weight_scale=5e-2, dropout=dropout, seed=13)\n",
    "\n",
    "    loss, grads = model.loss(X, y)\n",
    "    print('初始化 loss: ', loss)\n",
    "\n",
    "    for name in sorted(grads):\n",
    "        f = lambda _: model.loss(X, y)[0]\n",
    "        grad_num = eval_numerical_gradient(f, model.params[name], verbose=False, h=1e-5)\n",
    "        print('%s 相对误差: %.2e' % (name, rel_error(grad_num, grads[name])))\n",
    "        \n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d943970",
   "metadata": {},
   "source": [
    "# trainer\n",
    "\n",
    "解耦神经网络训练过程，打开`chapter4\\trainer.py`阅读相关内容"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ce2b0e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(迭代 1 / 4900) 损失值: 4.680202\n",
      "(周期 0 / 20) 训练精度: 0.155000; 验证精度: 0.163000\n",
      "(迭代 201 / 4900) 损失值: 1.954245\n",
      "(周期 1 / 20) 训练精度: 0.335000; 验证精度: 0.361000\n",
      "(迭代 401 / 4900) 损失值: 1.626711\n",
      "(周期 2 / 20) 训练精度: 0.448000; 验证精度: 0.416000\n",
      "(迭代 601 / 4900) 损失值: 1.764400\n",
      "(周期 3 / 20) 训练精度: 0.460000; 验证精度: 0.446000\n",
      "(迭代 801 / 4900) 损失值: 1.525576\n",
      "(周期 4 / 20) 训练精度: 0.485000; 验证精度: 0.439000\n",
      "(迭代 1001 / 4900) 损失值: 1.405463\n",
      "(迭代 1201 / 4900) 损失值: 1.557814\n",
      "(周期 5 / 20) 训练精度: 0.493000; 验证精度: 0.453000\n",
      "(迭代 1401 / 4900) 损失值: 1.352051\n",
      "(周期 6 / 20) 训练精度: 0.528000; 验证精度: 0.484000\n",
      "(迭代 1601 / 4900) 损失值: 1.141143\n",
      "(周期 7 / 20) 训练精度: 0.538000; 验证精度: 0.459000\n",
      "(迭代 1801 / 4900) 损失值: 1.314146\n",
      "(周期 8 / 20) 训练精度: 0.553000; 验证精度: 0.468000\n",
      "(迭代 2001 / 4900) 损失值: 1.327610\n",
      "(迭代 2201 / 4900) 损失值: 1.221952\n",
      "(周期 9 / 20) 训练精度: 0.555000; 验证精度: 0.485000\n",
      "(迭代 2401 / 4900) 损失值: 1.177750\n",
      "(周期 10 / 20) 训练精度: 0.530000; 验证精度: 0.490000\n",
      "(迭代 2601 / 4900) 损失值: 1.410634\n",
      "(周期 11 / 20) 训练精度: 0.621000; 验证精度: 0.488000\n",
      "(迭代 2801 / 4900) 损失值: 1.281012\n",
      "(周期 12 / 20) 训练精度: 0.592000; 验证精度: 0.506000\n",
      "(迭代 3001 / 4900) 损失值: 1.151491\n",
      "(周期 13 / 20) 训练精度: 0.612000; 验证精度: 0.489000\n",
      "(迭代 3201 / 4900) 损失值: 1.117616\n",
      "(迭代 3401 / 4900) 损失值: 1.085048\n",
      "(周期 14 / 20) 训练精度: 0.626000; 验证精度: 0.502000\n",
      "(迭代 3601 / 4900) 损失值: 1.073987\n",
      "(周期 15 / 20) 训练精度: 0.603000; 验证精度: 0.514000\n",
      "(迭代 3801 / 4900) 损失值: 1.046622\n",
      "(周期 16 / 20) 训练精度: 0.587000; 验证精度: 0.490000\n",
      "(迭代 4001 / 4900) 损失值: 1.064303\n",
      "(周期 17 / 20) 训练精度: 0.620000; 验证精度: 0.515000\n",
      "(迭代 4201 / 4900) 损失值: 0.937421\n",
      "(迭代 4401 / 4900) 损失值: 1.072491\n",
      "(周期 18 / 20) 训练精度: 0.647000; 验证精度: 0.517000\n",
      "(迭代 4601 / 4900) 损失值: 1.024862\n",
      "(周期 19 / 20) 训练精度: 0.641000; 验证精度: 0.498000\n",
      "(迭代 4801 / 4900) 损失值: 0.904784\n",
      "(周期 20 / 20) 训练精度: 0.644000; 验证精度: 0.515000\n"
     ]
    }
   ],
   "source": [
    "model = None\n",
    "trainer = None\n",
    "\n",
    "D,H,C,std,r= 3*32*32,200,10,1e-2,0.6\n",
    "model = FullyConnectedNet(input_dim=D, hidden_dims=[H], num_classes=C, weight_scale=std)\n",
    "trainer = Trainer(model,data,update_rule='sgd',\n",
    "                updater_config={'learning_rate': 1e-3,},\n",
    "                lr_decay=0.95,num_epochs=20, batch_size=200,print_every=200)\n",
    "trainer.train()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "44fe4385",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 可视化训练/验证结果\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(trainer.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(trainer.train_acc_history, '-o', label='train')\n",
    "plt.plot(trainer.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(trainer.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bba8cbac",
   "metadata": {},
   "source": [
    "# 正则化实验\n",
    "\n",
    "使用500训练数据进行正则化实验"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d7986fa7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dropout激活概率(0表示不使用dropout)0.000000:\n",
      "(迭代 1 / 150) 损失值: 9.543583\n",
      "(周期 0 / 30) 训练精度: 0.198000; 验证精度: 0.154000\n",
      "(周期 1 / 30) 训练精度: 0.276000; 验证精度: 0.200000\n",
      "(周期 2 / 30) 训练精度: 0.382000; 验证精度: 0.246000\n",
      "(周期 3 / 30) 训练精度: 0.408000; 验证精度: 0.207000\n",
      "(周期 4 / 30) 训练精度: 0.608000; 验证精度: 0.271000\n",
      "(周期 5 / 30) 训练精度: 0.722000; 验证精度: 0.281000\n",
      "(周期 6 / 30) 训练精度: 0.738000; 验证精度: 0.279000\n",
      "(周期 7 / 30) 训练精度: 0.812000; 验证精度: 0.271000\n",
      "(周期 8 / 30) 训练精度: 0.910000; 验证精度: 0.273000\n",
      "(周期 9 / 30) 训练精度: 0.892000; 验证精度: 0.312000\n",
      "(周期 10 / 30) 训练精度: 0.956000; 验证精度: 0.282000\n",
      "(周期 11 / 30) 训练精度: 0.944000; 验证精度: 0.287000\n",
      "(周期 12 / 30) 训练精度: 0.982000; 验证精度: 0.289000\n",
      "(周期 13 / 30) 训练精度: 0.986000; 验证精度: 0.305000\n",
      "(周期 14 / 30) 训练精度: 0.994000; 验证精度: 0.290000\n",
      "(周期 15 / 30) 训练精度: 1.000000; 验证精度: 0.297000\n",
      "(周期 16 / 30) 训练精度: 0.998000; 验证精度: 0.289000\n",
      "(周期 17 / 30) 训练精度: 0.996000; 验证精度: 0.293000\n",
      "(周期 18 / 30) 训练精度: 0.996000; 验证精度: 0.293000\n",
      "(周期 19 / 30) 训练精度: 1.000000; 验证精度: 0.298000\n",
      "(周期 20 / 30) 训练精度: 1.000000; 验证精度: 0.300000\n",
      "(周期 21 / 30) 训练精度: 1.000000; 验证精度: 0.297000\n",
      "(周期 22 / 30) 训练精度: 1.000000; 验证精度: 0.292000\n",
      "(周期 23 / 30) 训练精度: 1.000000; 验证精度: 0.294000\n",
      "(周期 24 / 30) 训练精度: 1.000000; 验证精度: 0.301000\n",
      "(周期 25 / 30) 训练精度: 1.000000; 验证精度: 0.296000\n",
      "(周期 26 / 30) 训练精度: 1.000000; 验证精度: 0.303000\n",
      "(周期 27 / 30) 训练精度: 1.000000; 验证精度: 0.304000\n",
      "(周期 28 / 30) 训练精度: 1.000000; 验证精度: 0.298000\n",
      "(周期 29 / 30) 训练精度: 1.000000; 验证精度: 0.299000\n",
      "(周期 30 / 30) 训练精度: 1.000000; 验证精度: 0.294000\n",
      "dropout激活概率(0表示不使用dropout)0.300000:\n",
      "(迭代 1 / 150) 损失值: 16.499589\n",
      "(周期 0 / 30) 训练精度: 0.190000; 验证精度: 0.169000\n",
      "(周期 1 / 30) 训练精度: 0.342000; 验证精度: 0.216000\n",
      "(周期 2 / 30) 训练精度: 0.438000; 验证精度: 0.260000\n",
      "(周期 3 / 30) 训练精度: 0.422000; 验证精度: 0.242000\n",
      "(周期 4 / 30) 训练精度: 0.548000; 验证精度: 0.260000\n",
      "(周期 5 / 30) 训练精度: 0.576000; 验证精度: 0.272000\n",
      "(周期 6 / 30) 训练精度: 0.612000; 验证精度: 0.255000\n",
      "(周期 7 / 30) 训练精度: 0.680000; 验证精度: 0.288000\n",
      "(周期 8 / 30) 训练精度: 0.700000; 验证精度: 0.286000\n",
      "(周期 9 / 30) 训练精度: 0.712000; 验证精度: 0.291000\n",
      "(周期 10 / 30) 训练精度: 0.764000; 验证精度: 0.310000\n",
      "(周期 11 / 30) 训练精度: 0.792000; 验证精度: 0.294000\n",
      "(周期 12 / 30) 训练精度: 0.838000; 验证精度: 0.302000\n",
      "(周期 13 / 30) 训练精度: 0.792000; 验证精度: 0.306000\n",
      "(周期 14 / 30) 训练精度: 0.834000; 验证精度: 0.301000\n",
      "(周期 15 / 30) 训练精度: 0.832000; 验证精度: 0.277000\n",
      "(周期 16 / 30) 训练精度: 0.858000; 验证精度: 0.288000\n",
      "(周期 17 / 30) 训练精度: 0.860000; 验证精度: 0.298000\n",
      "(周期 18 / 30) 训练精度: 0.894000; 验证精度: 0.319000\n",
      "(周期 19 / 30) 训练精度: 0.894000; 验证精度: 0.294000\n",
      "(周期 20 / 30) 训练精度: 0.902000; 验证精度: 0.300000\n",
      "(周期 21 / 30) 训练精度: 0.900000; 验证精度: 0.300000\n",
      "(周期 22 / 30) 训练精度: 0.928000; 验证精度: 0.309000\n",
      "(周期 23 / 30) 训练精度: 0.932000; 验证精度: 0.323000\n",
      "(周期 24 / 30) 训练精度: 0.932000; 验证精度: 0.302000\n",
      "(周期 25 / 30) 训练精度: 0.934000; 验证精度: 0.308000\n",
      "(周期 26 / 30) 训练精度: 0.964000; 验证精度: 0.294000\n",
      "(周期 27 / 30) 训练精度: 0.962000; 验证精度: 0.294000\n",
      "(周期 28 / 30) 训练精度: 0.960000; 验证精度: 0.294000\n",
      "(周期 29 / 30) 训练精度: 0.952000; 验证精度: 0.307000\n",
      "(周期 30 / 30) 训练精度: 0.948000; 验证精度: 0.320000\n",
      "dropout激活概率(0表示不使用dropout)0.700000:\n",
      "(迭代 1 / 150) 损失值: 11.059549\n",
      "(周期 0 / 30) 训练精度: 0.162000; 验证精度: 0.153000\n",
      "(周期 1 / 30) 训练精度: 0.198000; 验证精度: 0.133000\n",
      "(周期 2 / 30) 训练精度: 0.430000; 验证精度: 0.237000\n",
      "(周期 3 / 30) 训练精度: 0.482000; 验证精度: 0.227000\n",
      "(周期 4 / 30) 训练精度: 0.598000; 验证精度: 0.276000\n",
      "(周期 5 / 30) 训练精度: 0.654000; 验证精度: 0.264000\n",
      "(周期 6 / 30) 训练精度: 0.716000; 验证精度: 0.282000\n",
      "(周期 7 / 30) 训练精度: 0.728000; 验证精度: 0.255000\n",
      "(周期 8 / 30) 训练精度: 0.740000; 验证精度: 0.285000\n",
      "(周期 9 / 30) 训练精度: 0.856000; 验证精度: 0.278000\n",
      "(周期 10 / 30) 训练精度: 0.888000; 验证精度: 0.286000\n",
      "(周期 11 / 30) 训练精度: 0.886000; 验证精度: 0.270000\n",
      "(周期 12 / 30) 训练精度: 0.918000; 验证精度: 0.296000\n",
      "(周期 13 / 30) 训练精度: 0.930000; 验证精度: 0.307000\n",
      "(周期 14 / 30) 训练精度: 0.950000; 验证精度: 0.285000\n",
      "(周期 15 / 30) 训练精度: 0.968000; 验证精度: 0.288000\n",
      "(周期 16 / 30) 训练精度: 0.968000; 验证精度: 0.290000\n",
      "(周期 17 / 30) 训练精度: 0.978000; 验证精度: 0.297000\n",
      "(周期 18 / 30) 训练精度: 0.986000; 验证精度: 0.281000\n",
      "(周期 19 / 30) 训练精度: 0.970000; 验证精度: 0.296000\n",
      "(周期 20 / 30) 训练精度: 0.986000; 验证精度: 0.298000\n",
      "(周期 21 / 30) 训练精度: 0.986000; 验证精度: 0.290000\n",
      "(周期 22 / 30) 训练精度: 0.992000; 验证精度: 0.298000\n",
      "(周期 23 / 30) 训练精度: 0.992000; 验证精度: 0.311000\n",
      "(周期 24 / 30) 训练精度: 0.992000; 验证精度: 0.311000\n",
      "(周期 25 / 30) 训练精度: 1.000000; 验证精度: 0.309000\n",
      "(周期 26 / 30) 训练精度: 0.990000; 验证精度: 0.299000\n",
      "(周期 27 / 30) 训练精度: 0.996000; 验证精度: 0.304000\n",
      "(周期 28 / 30) 训练精度: 0.998000; 验证精度: 0.312000\n",
      "(周期 29 / 30) 训练精度: 0.998000; 验证精度: 0.314000\n",
      "(周期 30 / 30) 训练精度: 0.998000; 验证精度: 0.306000\n"
     ]
    }
   ],
   "source": [
    "num_train = 500\n",
    "small_data = {\n",
    "    'X_train': data['X_train'][:num_train],\n",
    "    'y_train': data['y_train'][:num_train],\n",
    "    'X_val': data['X_val'],\n",
    "    'y_val': data['y_val'],\n",
    "}\n",
    "\n",
    "solvers = {}\n",
    "dropout_choices = [0, 0.3,0.7]\n",
    "for dropout in dropout_choices:\n",
    "    model = FullyConnectedNet(hidden_dims=[600], dropout=dropout)\n",
    "    print(\"dropout激活概率(0表示不使用dropout)%f:\"%dropout)\n",
    "\n",
    "    trainer = Trainer(model, small_data,\n",
    "                                    num_epochs=30, batch_size=100,\n",
    "                                    update_rule='sgd',\n",
    "                                    updater_config={\n",
    "                                        'learning_rate': 5e-4,\n",
    "                                    },\n",
    "                                    verbose=True, print_every=200)\n",
    "    trainer.train()\n",
    "    solvers[dropout] = trainer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "da9b2069",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_accs = []\n",
    "val_accs = []\n",
    "for dropout in dropout_choices:\n",
    "    solver = solvers[dropout]\n",
    "    train_accs.append(solver.train_acc_history[-1])\n",
    "    val_accs.append(solver.val_acc_history[-1])\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "for dropout in dropout_choices:\n",
    "    plt.plot(solvers[dropout].train_acc_history, 'o', label='%.2f dropout' % dropout)\n",
    "plt.title('Train accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "    \n",
    "plt.subplot(3, 1, 2)\n",
    "for dropout in dropout_choices:\n",
    "    plt.plot(solvers[dropout].val_acc_history, 'o', label='%.2f dropout' % dropout)\n",
    "plt.title('Val accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(ncol=2, loc='lower right')\n",
    "\n",
    "plt.gcf().set_size_inches(15, 15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5a831fb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(迭代 1 / 4900) 损失值: 2.310505\n",
      "(周期 0 / 50) 训练精度: 0.118000; 验证精度: 0.092000\n",
      "(周期 1 / 50) 训练精度: 0.242000; 验证精度: 0.240000\n",
      "(周期 2 / 50) 训练精度: 0.277000; 验证精度: 0.275000\n",
      "(周期 3 / 50) 训练精度: 0.267000; 验证精度: 0.301000\n",
      "(迭代 301 / 4900) 损失值: 2.048633\n",
      "(周期 4 / 50) 训练精度: 0.310000; 验证精度: 0.312000\n",
      "(周期 5 / 50) 训练精度: 0.345000; 验证精度: 0.332000\n",
      "(周期 6 / 50) 训练精度: 0.327000; 验证精度: 0.339000\n",
      "(迭代 601 / 4900) 损失值: 1.893793\n",
      "(周期 7 / 50) 训练精度: 0.338000; 验证精度: 0.353000\n",
      "(周期 8 / 50) 训练精度: 0.353000; 验证精度: 0.363000\n",
      "(周期 9 / 50) 训练精度: 0.389000; 验证精度: 0.363000\n",
      "(迭代 901 / 4900) 损失值: 1.817221\n",
      "(周期 10 / 50) 训练精度: 0.385000; 验证精度: 0.375000\n",
      "(周期 11 / 50) 训练精度: 0.355000; 验证精度: 0.380000\n",
      "(周期 12 / 50) 训练精度: 0.366000; 验证精度: 0.383000\n",
      "(迭代 1201 / 4900) 损失值: 1.807873\n",
      "(周期 13 / 50) 训练精度: 0.331000; 验证精度: 0.388000\n",
      "(周期 14 / 50) 训练精度: 0.405000; 验证精度: 0.394000\n",
      "(周期 15 / 50) 训练精度: 0.364000; 验证精度: 0.395000\n",
      "(迭代 1501 / 4900) 损失值: 1.802722\n",
      "(周期 16 / 50) 训练精度: 0.403000; 验证精度: 0.394000\n",
      "(周期 17 / 50) 训练精度: 0.375000; 验证精度: 0.398000\n",
      "(周期 18 / 50) 训练精度: 0.378000; 验证精度: 0.400000\n",
      "(迭代 1801 / 4900) 损失值: 1.828593\n",
      "(周期 19 / 50) 训练精度: 0.404000; 验证精度: 0.410000\n",
      "(周期 20 / 50) 训练精度: 0.405000; 验证精度: 0.403000\n",
      "(周期 21 / 50) 训练精度: 0.390000; 验证精度: 0.407000\n",
      "(迭代 2101 / 4900) 损失值: 1.781108\n",
      "(周期 22 / 50) 训练精度: 0.394000; 验证精度: 0.407000\n",
      "(周期 23 / 50) 训练精度: 0.373000; 验证精度: 0.416000\n",
      "(周期 24 / 50) 训练精度: 0.407000; 验证精度: 0.411000\n",
      "(迭代 2401 / 4900) 损失值: 1.807947\n",
      "(周期 25 / 50) 训练精度: 0.409000; 验证精度: 0.409000\n",
      "(周期 26 / 50) 训练精度: 0.404000; 验证精度: 0.419000\n",
      "(周期 27 / 50) 训练精度: 0.398000; 验证精度: 0.417000\n",
      "(迭代 2701 / 4900) 损失值: 1.802167\n",
      "(周期 28 / 50) 训练精度: 0.403000; 验证精度: 0.418000\n",
      "(周期 29 / 50) 训练精度: 0.392000; 验证精度: 0.418000\n",
      "(周期 30 / 50) 训练精度: 0.410000; 验证精度: 0.421000\n",
      "(迭代 3001 / 4900) 损失值: 1.762628\n",
      "(周期 31 / 50) 训练精度: 0.389000; 验证精度: 0.425000\n",
      "(周期 32 / 50) 训练精度: 0.388000; 验证精度: 0.422000\n",
      "(周期 33 / 50) 训练精度: 0.400000; 验证精度: 0.424000\n",
      "(迭代 3301 / 4900) 损失值: 1.778770\n",
      "(周期 34 / 50) 训练精度: 0.417000; 验证精度: 0.431000\n",
      "(周期 35 / 50) 训练精度: 0.395000; 验证精度: 0.434000\n",
      "(周期 36 / 50) 训练精度: 0.401000; 验证精度: 0.431000\n",
      "(迭代 3601 / 4900) 损失值: 1.701707\n",
      "(周期 37 / 50) 训练精度: 0.429000; 验证精度: 0.428000\n",
      "(周期 38 / 50) 训练精度: 0.401000; 验证精度: 0.432000\n",
      "(周期 39 / 50) 训练精度: 0.399000; 验证精度: 0.432000\n",
      "(迭代 3901 / 4900) 损失值: 1.819598\n",
      "(周期 40 / 50) 训练精度: 0.408000; 验证精度: 0.434000\n",
      "(周期 41 / 50) 训练精度: 0.413000; 验证精度: 0.431000\n",
      "(周期 42 / 50) 训练精度: 0.407000; 验证精度: 0.435000\n",
      "(迭代 4201 / 4900) 损失值: 1.774587\n",
      "(周期 43 / 50) 训练精度: 0.458000; 验证精度: 0.428000\n",
      "(周期 44 / 50) 训练精度: 0.416000; 验证精度: 0.433000\n",
      "(周期 45 / 50) 训练精度: 0.415000; 验证精度: 0.433000\n",
      "(迭代 4501 / 4900) 损失值: 1.758987\n",
      "(周期 46 / 50) 训练精度: 0.421000; 验证精度: 0.434000\n",
      "(周期 47 / 50) 训练精度: 0.433000; 验证精度: 0.436000\n",
      "(周期 48 / 50) 训练精度: 0.427000; 验证精度: 0.437000\n",
      "(迭代 4801 / 4900) 损失值: 1.767904\n",
      "(周期 49 / 50) 训练精度: 0.412000; 验证精度: 0.435000\n",
      "(周期 50 / 50) 训练精度: 0.412000; 验证精度: 0.440000\n"
     ]
    },
    {
     "data": {
      "image/png": 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oi2IKZQlEakLY05ws6DG0ypWxeAJT5zdi6vxGAECfmhBmXTGKhU2IiIiIiLqAvGfgRGSwiCwVkTdFZLWI3GFwm5Ei8qKIHBaRrxW2q5WpviGGA4dai/b4e5qTmPbXVZyVIyIiIiLqAgpJoWwFcJdS6hQAEwB8UUROzbrNbgBfAfCjArZT0eYtWYtkWyGd3+wlUwrzlqwt6jaIiIiIiKj88g7glFLblFKvZv69H8CbAKJZt9mulHoFQGH5gxWs0PVvftsOERERERGVjydFTERkKIBaAC8V8Bi3icgKEVmxY8cOL3bLFwZGwp1qO0REREREVD4FB3Ai0hPAAgBTlVL78n0cpdSDSqnxSqnxAwYMKHS3fGPa5BEIh4JF3UYwIJg2eURRt0FEREREROVXUAAnIiGkg7dHlFILvdmlzqWuNoo514xGUKRo2+jVrYpVKImIiIiIuoBCqlAKgN8CeFMp9WPvdqnzqauN4r4bxhRtJm5vossuMSQiIiIi6lIK6QM3EcAtAJpEpDFz2d0AhgCAUupXInIsgBUAjgLQJiJTAZxaSKplpdJmyO6c3wiva1Jy/RsRERERUdeQdwCnlHoegGVeoFLqPQCD8t1GZ6MFcdMeX+VZa4EQ178REREREXUZhczAUR60IG7ekrWIeVD6v63gRyAiIiIiokrhSRsBcqeuNopl0yd58lipNoV7nlztyWMREREREZG/MYAro6hHa9f2NCdR+51nUN8Q8+TxiIiIiIjInxjAldG0ySMQCnjTXmBPcxLT/rqKQRwRERERUSfGAK6M6mqj6Nndu2WIyZTC7EVMpyQiIiIi6qxYxKTM4s3e9nCLJ9LplPHmJAZGwpg2eQSbfBMRERERdRKcgSuzYvRw29OchAIQiycwY2ET0yqJiIiIiDoJBnBlNm3yCIRDwaI9fiKZwrwla4v2+EREREREVDpMoSwzr/vCGdlapMclIiIiIqLS4gycD2h94bypR5mrGGmaRERERERUegzgfKRYgdaFIwcU5XGJiIiIiKi0GMD5SLHWwy1ds8PzxyQiIiIiotLjGjgf0dbDTZ3f6OnjxuIJTJz7XHtLgfqGGOYtWYut8QR6h0MQgWXbAf3t2ZqAiIiIiKh8RClV7n3IMX78eLVixYpy70bZnDBjMdqK8LaEQ0FcOy6KBStjSCRTpreZc81o1NVGMbO+CY8s34TsXdHfhoiIiIiIvCciK5VS43MuZwDnL/UNMc9n4PSCIkjZvOeRcAiHW1NIJNtMbxONhLFs+iSvd4+IiIiIiGAewDGF0meK3bPNLngDgHgiaXsbtiYgIiIiIio9BnA+UymBkZ9aE3CNHhERERF1FQzgfGZgJFy0ht5eCYeCmDZ5hC8Cp/qGGGYsbGpf0xeLJzBjYRMAMIgjIiIiok6HbQR8plitBLxSHRR0DwUwdX4j7pzfiFg8AYUjgVN9Q6yk+zNvydqcgiyJZKroqahEREREROXAAM5n6mqjmHPNaETCobJsPxwKGAaQAQEmDu+LYCCAPc3pNXLZq+nKETiZpZxWSioqEREREZEbDOB8qK42ih7dypPdmki2IZFMISgCIF1t8oEpY/HunMuxYVfCtP2AptSBk9laPD+t0SMiIiIi8goDOJ8q9wxSSimEgtJhXZuTtXmlDpyMUk61NXpERERERJ0NAzif8sMMUjKlcM+TqwGki4WIze3LEThpKafRSBiC9Iwhm4wTERERUWfFKpQ+NW3yiA7VFctFW+82b8nanDVvetEylu+vq40yYCMiIiKiLoEBnE/V1UaxYuNuPLJ8k2XgVCpWKZ0b5l5ewj0hIiIiIuq6mELpY0vX7PBF8AaYp3SKoOStA4iIiIiIuirOwPlYuQuZAECfmnQ7g2mTR2Da46uQbOsYUioFTPvrKgD5N87WGoLH4gkERZBSqqwpmUREREREfsUZOB8rdyGTUFAw64pR7X+3thnPByZTKu/+b/UNMcxY2NRe4TKl0tsoV2NwIiIiIiI/YwDnY0Yl8ktJq0I5s74JMxY2WaZz5jtbOG/JWtNCLeVoDE5ERERE5GdMofQxLX1w9qLViCeSZdmHPc1JPLx8k+3t8p0ttAv8/JBGSkRERETkF5yB87m62igaZ12MB6aMbe91FgmHyr1bOfLt/2YX+JU7jZSIiIiIyE/yDuBEZLCILBWRN0VktYjcYXAbEZGfisg7IvKaiJxR2O52XXW1USybPgnr516OxlkXtxcX8Yt5S9bmtV7NKk20HI3BiYiIiIj8TJTKr1C9iBwH4Dil1Ksi0gvASgB1Sqk3dLe5DMCXAVwG4GwAP1FKnW332OPHj1crVqzIa7+6Cq34R7kbfWfrUxPC5acfh6VrdmBrPIGBJtUktcqTW+MJ9A6HIJJO12QVSiIiIiIiQERWKqXGZ1+e9xo4pdQ2ANsy/94vIm8CiAJ4Q3ezqwD8UaWjxOUiEhGR4zL3pQJogY1Wft8vstfMadUkgSP7nB18xhNJhENBPDBlLAM2IiIiIiILnqyBE5GhAGoBvJR1VRTAZt3fWzKXkQe0tMoNcy8v965YSiRTmDq/ERPnPtc+85Y9c8iKk0RERERE9goO4ESkJ4AFAKYqpfZlX21wF8OcTRG5TURWiMiKHTt2FLpbXU60Aop9aLNxZjOGfppJJCIiIiLyo4LaCIhICOng7RGl1EKDm2wBMFj39yAAW40eSyn1IIAHgfQauEL2q6vQZrNi8YRhpOxHiWSqfZ1bNkH6OZUyjVK/Fs9svR4RERERkV/kHcCJiAD4LYA3lVI/NrnZIgBfEpG/IF3EZC/Xv3kjex1ZJUW8RsEbkH4O85asNQygihFoZb+GRuv1nD4Og0AiIiIiKoVCUignArgFwCQRacz8d5mI3C4it2du83cA7wJ4B8D/AfhCYbtLGqN1ZJ2BUeNuLdCKxRNQSAdad85vxMz6poK25cVaPKN9m7GwKa+WCkREREREdgqpQvk8jNe46W+jAHwx322QOaNApxzM0iHzZdS42yjQUgAeWb4J44/vi7raqOEsmHZfs5kxs9fQzWtrFQRyFo6IiIiIvFbQGjgqn4GRcNmLfgiA+24Yg6/Ob0SbR49p1LjbLKDSUi4B5KRCTnt8FSBAMqXaL8tOjzR7DY2CSDNeBIFERERERE550kaASm/a5BEIh4Jl3Qct0PEqeIuEQx1mreobYhh7zzOW6/u2xhOGs2DJNtUevGmy0yONXsNwKGgYRJoxC/bcBIFERERERE5xBq5CZTfy1lIZvU5ptFJTHUjPdHlk1MBeGD7j7672f2Ak7Gq2S39bo9dQH+Q5SYGcNnlEh9k/wH0QSERERETkFAO4ClZXG80JMoZNX1yy7b+9/aCnj7ds3W5Xt9cCJS0AcyJSE8q57ODhVgBHqmO6qUapDwJZhZKIiIiIio0BXCfjh7VxpVATCqBbKIA75zeidziEgABtDibu9JN72W0E9LILkVi1CjAKpImIiIiIioFr4DoZP6yNK4VEsg17mpNQAOKJpKPgDQD2JpLt/7ZrxaClW7JVABERERH5BQO4TqauNoo514xGJJybKtiZ5LvKT19cxG7tnHZbL/rFERERERF5gQFcJ1RXG0WPbsyOzZZdXMSqUqT+tmwVQERERER+wVF+J8XgoqM+NSHMumJUhzVtWvESI9eOi+bVL85qrRyVD98XIiIi6iwYwHVSlV7MJBSUnD5uhaiprkJdbRT1DTHMXrQacd1aOCNL1+xo/7fTVgHZRVHcVLOk4uH7QkRERJ0JUyg7qWmTR0DKvRMFqAoIguLdM4jFE+0DebvgTbu9xmhdYfdQ7kfHbK3c1PmNmDj3ORY9KROuYSQiIqLOhAFcJ1VXG8VNE4aUezfylki2ed6Q/JtPGLcMMGIUPB5ubWv/957mJO6c34iZ9U3tl1mlrXb2ypX1DTFMnPschk1f7LtglWsYiYiIqDNhANeJ3Vs3Gn0MGld3VQdbnAVvAHKCR6NZHAXgkeWb2oMVq6IoQOed9fF7mwWz98Xu/SIiIiLyIwZwnVy82T5dkHJFswb3ZrM1CmgPypz04OuMsz5+T1E0el+M1jASERERVQIGcJ2ck1mGzt4zzq1QUHIG91avoxaUaWvlsoM/p49TqfycoqhVn0wkU+1psdFIGHOuGc0CJkRERFSRGMB1cnazQqGA4GCLeTn9zu7mCUMQCnRc75ZMKdzz5OoOKYBWRWH0QVldbRTLpk/CA1PGOp718fP6MSf8mqKoT+0E0mmx2nvA4I2IiIgqFQO4Tk4/KyRIz7b1qQlBkJ6J6Nm9ytNy/ZUkGglj6ZodSLblPv89zckO67i0ojDZQZxZUJb9upvN+uSzfsxvAZ9fUxT9ntpJRERElA9RHlf688L48ePVihUryr0bXcKw6YvhvyOgNHpUB20Lm0QjYSybPqn9b68bQk+c+5xhv77s7eq3b9STrhgpgW6eqx8bZZsd2wJg/dzLS707RERERK6IyEql1Pjsy9nIu4ur9IbfhXBSlTL7tamrjXoamLhdP2Y1q5TvfhkFXwBcNb/2+nXxgtmxXe7UTiIiIqJCMIWyi3NSObEr87KZuFHqo1kw0duksIzXBUPMUjjveXJ1xacf+jW1k4iIiKgQDOC6OKO1WkaFPboqJ83EnaxJMwuULhw5wPC1PtjSavg4dgVD3K6PM5vR22PSfsIPlSWdcroOkYiIiKiScA0cGapviGHq/MZy74ZvRHXruvQph73DIRxsae1QCMZoTZrVWrfmllbDgMloHZzVGjgArtfHuV0DabY2r1j8uLaOiIiIqBTM1sAxgCNTQ6cvLvcuFCwg6XREsxmlYgmKoE2p9qDjzvmNpgU1ALgqtmEW1LgtiAKYB5aRcAiHW9tKUizFTCkLthARERH5DYuYkGt9akof+HitTaEsz0FLvYzFE7hzfiNqTCpe9g6HsP9Qq2Gqplm6pFnBkHzWx02bPMIwSJp95SgAKOvsVzEKthARERFVOgZwZGrWFaMw7a+rumyfOK8opCtehoLS4bXUmqgbBW/5FNtwW3VRm8lLJFMIiiClVIdUUcC44qTRYxQjyPO6YAsRERFRZ8AiJmSqrjaKedeNQdSm7HqfGuOKiZWkJhRAxKTyo1dadcFbn5qQZRP1a8e5L8vvpuqivqgKkJ4x1G7rdLv5NCF3w65gCxG547bIERER+RMDOLJUVxvFsumTYFWTsuHbF+OBKWMruh1BdVUQjbPSz8MuYM2XPlQ7lGyzTO1cumaH68d3U3XRKj3RKS8ewwrbAHiHA3cq9gkXIiIqHaZQkiNm6XlBEQybvhgDI2FcOy6KpWt2VGRj8L2JdDClrS+bWd+Eh5dvKtr29GmLRvJ5Dc0ack+c+1xOiqMX6YnFTnHUAs/OUoWyXBU1s4vB2DVlLxVWGC0triklIuo8WIWSHDGqCJhNANw0YQgWv7at4oqfaFUje4dDEClP4ZNsUV0Qpm9bIALEm5MdBr1G708oIIDAsMXBvCVrXVeszJZP1cuuyuj90T4v99aNLuq2/fg+scJo6Zm1DDGrdktEROXHKpRUkOzZkIDB7JEC8MjyTa76ivmF9lziCWeBWzgUtAxmnYiazGpqYvFETi8+/f7pZ1KMzq4n23LfCe2Mu1n1yWmTRzieGbF6jHwVc1amnDM+Ru+P9nkZf3zfou6HH4vBcDao9NwWOSIiIv/iDBzlpTP0iCuUVQqknVBAMO/6MaYzYW7YBYJGBDCczQPcNQPPbmpuNDtoR3uMWDwBQce1gl7MytQ3xDB70eqc4LyYMz7ZwaLV+1PsmTA/zsBxNqj0OOtJRFR5zGbgCipiIiK/E5HtIvK6yfV9ROQJEXlNRF4WkdMK2R75R1Csypp0DSmlLIu72N0XMC7U4ZYW+LihkJ7NO5Rsw/1TxmLZ9Emoq43inidXuypMohW5uX/KWBxuTRdmcVMgIbsaZvagvtCiKNrjG82sellwxWib+mIRVu9PsWfC/FgMhhVGS89NkSMiIvK3QlMoHwLwMwB/NLn+bgCNSqmrRWQkgJ8DuKjAbZIP5Dvz1NlUBYBkW8fLBMCJR/fA29sPmt6vTQGzF61G46yLASAnVdINq5nA7N5z2fRpa/UNMdO1f3ZBRr4pcUb3c7ttjVGKpN3jO3lst6mXZumSZoodtPilGEz2bG32sVnuoLIr0Io0EeWDhYeI/KOgAE4p9V8RGWpxk1MBzMncdo2IDBWRY5RS7xeyXSq/fNL27ETCIRxubSt4bVkpZQdvfWpCmHXFKMxetNr2vvFEEvUNsXS/vTxTKQXWwfS868a0/+Ca3UoLYqxmo3rb9Mhzs85KPwhwchrAbtvaY2ZXWrxzfqPt4xsFT9mBxsGW1vZAw0kFR6ug0ChFtBRBS7kH7tnvTzyRRCgg6FMTcp1yS0Sl59dqtkRdVbH7wK0CcA0AiMhZAI4HMKjI26QS8CL1Ty8UEIgcKa9fqfY0JzF1fqPjYihT5zdi6PTFOHi4FQGXTzs7GMgWjYTbUxzXz73ctL+dFsRYBh42++Y0Ja6+IYZpj69qTy90Ip5I4oQZizGzvsn0Nm5nvQDj4Ck7/TGeSObMYNqlXpq9FtFIGPdn+gx2tRQ2syI7NdVVWD/38vYUXiK/YQ/FtGL3/SQid4pdhXIugJ+ISCOAJgANAFqNbigitwG4DQCGDBlS5N2iQmWnZWXPVLghANpwpHR/V0zPdBrwaeyCN6OKknZpa1bFNvY0J9v7/RnNlDitagmb/TbTptDel8+o7L6bWS/gyExp9vNwktJptz2r16LcM2EaN6lQXqRN+bESJpEdzjodwc8wkb8UXIUyk0L5lFLKskCJiAiA9QBOV0rts7otq1BWppn1TRXbRqCz6VMTwuWnH4cFK2OWAUlQALcxt1n/suxqjzWh9AR/c3aeaQGCIlg357Kc7d712CrLwD8aCTsKQMyqI2brUxNCw7cvNr3ez2tF3FQjNLvtteOiWLpmh+Pn58dKmER2eNwewdeCqDzK0gdORCIAmpVSLQBuBfBfu+CNKtfSNTsYvPnEnuako2A6jwlTy/5lh1uPBGteBm6a7CBNCzDsgjenAwy7kv8au/NefplpM+Km4IzZbfXHlpNZiWL0DCQq9okSq1knP5+kKQZ+hon8pdA2Ao8CeBHACBHZIiKfEZHbReT2zE1OAbBaRNYAuBTAHYXtLvkZUyn8pZjBtEJu0ROn6YeFyF4fabdNAVwNMKZNHuHoS3Gvy5RXP3GTCmV2W7ftHljCnrxm1K7DSesSN8zWs/YOh4q+bb/J/gxHwiF0DwVw5/zGLr02kKhcCq1CeaPN9S8COKmQbVDlcDJ7EZD0eiZyJhwKIFGEmSwvZA/uSxHATzihT3sqj906QGSuNwsSZtY34dGXNiOlFIIiuPHswbi3bjS++lij7QPnW/q/lGftzbZl9jk1ek5OZyQB+/ff6axkV5vZoPzk27rEDbNZJ63gVjG37UfaZ5hrA4nKr9hFTKgLMfqx0wbZkXAIIkC8OYmIQcGTUEAQCkpR0u4qmV+DN+DIgF8bcBc7Lj/p6B54ddPe9uPLyfbMKm/OrG9qL4oCpFMztb/tTjDkmzZUykGPWVuFqfMbEXHRg83oM20m+3jIJwAze41WbNztas0ddX6lKKph1kPxTpO+nV0lC6UUwXNXxJNX5AYDOPKM2Y8dAMc9oIy+wFZs3N1hsN2ZWTXl9hutwqTTAX4hakIBNLe46xFoFWg9+tJmw8vtjrNoAT+q+Qx68v1Bt2qr4KYHW11tFCs27rZdT6mvOFpIkOrVmrvOhgO7XG5mkgthNHNs1rfT6237FStSeo+zmuQWAzjylNGP3cS5z5n2gLKq5KfRqh2aDSJDgXTQ45fUzFBAAEFeLRVKGbwJgFBQ0JJPJRMA9zyZblZeisbrzck2JFwMDuwCrXxe5x7VwYIGzm4HPYX8oNsNpNx8/uyKE+lfa6PPeiKZwuxFqx0FIG7X3HWFgY0XA7vOGACWs6hGVy/oUarguSvhrCa5xQCOis7JwDW7BD3QcaByb93o9kAuezDS3NLa3kPOD5J+iSQtZAc49Q0xfHV+I9wkbJb6NXeyHsuuvL8mn5nOgy2pDgNnt4Niu0GP0XHt5gddf/+Ag+fn9Gy51e2yK3ya3TaeSLZ/tq0CEC/X3HUWhQ7sOtOZ/ezPiNt2Fl4xyzaptNczX109gC0GL2c1O+MJG8rFAI6KzsnA1SwNz2igkj3LN3T64iLsdecWiycwNbMmqk9NCABcBW+lVhMK4MKRA2xTHJ3EZPUNMVRXCRJJ94G2vtqi3aA4+0f0wpEDcvryhQKC5pZWDJ2+uENRFqsgxugHPfsz5CQ4dXq23Cqoyt4XpwGYWQBitY7WaL/cqsSBTaEDu85yZt8oEF2wMla2aqZ+bhVSbJ0tgPXD94JXs5qd6YQNWWMAR0Vnd7bOrhR8LNNzx+zLp5LWjflRKWfS8n2vqquCWPzaNtvbaeX9zX6Qj/y45R+ubo0nbAfFZoNN/YxB70wxH+31d/qqGP2gm32GRNJBrVEQFIsn2k9+9KkJYdYVoww/Y1rRBidB1IUjBzjqPwgYByBGA0OjwFcy+z9x7nOOB1tuBjZ+GNBprAZ2TvbTLNCLxRMYNn2xq+dXztelswSi5VCM960zBLB2mT+lfH5ezWqW83Pip+/NroABHBWd3dk6J2eSrb5QGbxVjpRS6FMTch00xh32XdMGtWYDdS961Q2MhG1nRcx+RJ9atQ09uqW/dvcfas3r2I03t2BmfVOH1DGzWS+lgJsnDMH44/uaFl4A0kH8tL+uApD7GTMrZCJIB2ya+oYYFqyM5dympjqIgy25r7nZmWWjgaF+/7NnKp0OtpwObIp5BjufAY7ZwO7CkQMc7afl8WFxP6N9L+eZfaep+BxAdlTu961U3L73bjN/is2rWc1yFZjpKseZn4jy4eB3/PjxasWKFeXeDSoRra+XHW29TfZZM/aWqyxGRV6c9HRzok9NCC2tbYYBQzQTeBWynVBAMO/6MabBkHaMDpu+uOhtFfSsXj8BcP+UsairjaL2O89YBs/Za9r0ZtY35QRx4VCwfVbR7DMcCYdwuLUtJwDJJ/XN7LvCar81Zu+J9vpoAyeIcSpuJBxC46z0+ko3g0Xttka9C52+DkbbszsG9fd1UilWf79Ctuf2eTg9Buzee6Pnme9x5oYfejtaKeQzUynyee/txh0CYP3cy73e1aIr1/vdFY6zchGRlUqp8dmXcwaOys5pr6lYPIGZ9U2Y//LmDoVCGLxVlmSbQiQcQo9uVZZpcvmwCk7sThJEI2HEm1sMgz9Nsk056qXWOxxyPGvoBauPgALa1+3ZzXxanaU1qkaZSKZs1yXuTSQ7BEiFDHILObtsNhPVOxzq+P1j8mLGE0nUN8QA2K9/1GQPLM2qaQLWZ96NZiWnmvQiy36O2Wf2zY4VLVXd7PmZfTadntkv9Ax9Pqn4iWQKdz22CnfObyxKcFXKCqH5bqsrlPzPJ23Q7vlXakXNchWY6QrHmd8Eyr0DRHW1Ucy5ZjQi4ZDtbR9evsm0ymNQBIL0QPyBKWPxwJSxCAXF470lL+xNJLFs+iTcP2UsgPT7eqgE7QjM9KkJYdn0SQgFnX0lxhNJQKWLq2gOt6YwdX4jhs/4e0mDNydimXV7dqwGLfn+EA+MhFFXG8W0ySPa00/nLVnbHiy4fSw3l+tNmzwC4VCww2XhUBAizlthzFuy1nKwaHR7JyemZixsQiwTXGl/270+QTH+bsu+PDtA0IoWGZmxsAn3PLna8PmZbc/Ja1/fEMNdj61y/LoZqauN4tpx0fb9CIrg2nFHAluz4zOllKvX1Q03x4IRLShz8t7nu61CPjOVIp/gwer5V3JFTW08FY2E28dDpSj00xWOM7/hDByVVfbg4iNjjsNTq7blNQBuU6pDykN9Q8ybvDzyXEAkp/JiOd+qeGZmaq+L4y7ZptBqMBNcrjWZdmmoTtKU9WvaskXyWLtoVWXTzUyFVRoiADS3tFoWOtJvJ3u2406TmSwjVgNCo+ucBL1GAaQ2c6Tf72xmx5n+cqNZm1BAcmaP9ds1CzhTShnOOl84cgAmzn3OcAbJqEhENjczeAtWxtqfX0opLFgZw/jj+6KuNuqoAqrXa5tKWSE03215MSPj97WF+VRwNMv8sSroVCnKUWDGrIKw1W9KKfj92C0EAzgqG6uy0Pc8udr1YFH/Za2d8fVyMH3zhCGOK+yRNe198ctrqR07bvqQAf7Zf8CbfXn0pc14ZPkmw4H4gUOtrh4rYlNl0+lg2i4NEUinhk6d34hvPtGE5paU6Q+10cDGqrhLtoEWabZaAZ3s9blWX0GhgJhmFKSUsgxyoybHalT3PWgUIGgpzHnNEqv0ADfenDRMfdYH5gAcpcY7PUNvF+y4ScU3Czid0gaFZm+t0+fkJijLt8x8ocUxKqE4RT5Bqrbv+s9rZwje3PAyuDEqdqWADidZSq0Sjt1CMICjsrH6QY7nUdpe+7LWPrReBm+hQLqZuFWxBio+qwFvvvQl6b1ai1eptM+M1ifwnidXY9YVozBvyVrHr7tWPGDekrW2QYKTmQo3lUO1wCo7kLAapDkd+IdDQQztF8Yyg30OBgQXjhyAaY+vcrU+t2f3KssTVVZBrpNBq9nruzeRNA0Aw6EAADF8PZJtCjXVVWj4drqYy8S5z1mm9Tl5TZ3OBNkFO9mBilkze+3zDuS/Zs3qeHHznNwEZYXMpBUyI1MJ7RsKCVIPtx5pKbOnOVnWirOl3E6+wY3V9szWSZfrWKmEY7cQDOCobKx+kN3OhAAdv8S9HoAn29JfXFY9saj4vA7egI5pfVqvtkde2mTbFNyrypmaYECQ8llFHm1A4/TzFBBgzjWjAThL2XQyU5Hv2rtEMoXZi1bj4OHWDsdNdsuE7MFf73AIIunbaX0Lo5mBipbWmK2tTWHpmh2ujk+tKqods9s4GbRaBQjTJo/ICTgBoLVNYcqZg0yL0+j3p5AUQu1YcTrYNAvI9MeQ/v00CrSMPrNOC8lorH5foi4H6m6CsmI0zy6kj6CT9zifaq35Prd8gtRiDfBLNfNTyHbyee522/NbIRO7/an09EoGcFQ2+QwuzOjThor1ZTFvyVosmz7JsCcWdQ6JZApL1+xA7+7WKWZ9akI49bheeGHd7oKPA21GCHCXzlcqbk6GHNU9XSBDP/tlJiDptWt2zaTzWXunMXsPkynVYaDidPBnVvlRK0DhlACWpfn1rIJcu/02+h4NBaT9tTZKVU+mFB59abNpv0b9/tjNIFk9t6O6h1zNFBgFb1YzUEYpclYVOJ0OhM1+XwRwXS7dbVBm9X4X2gctFk/gzvmNmDq/scOJC7PPn93JFzfBRal7LwLW37WFjiFKNfPjZDtmzz+f5263vXzTfIvFan86Q3olAzgqG6uzj0Y/vkC66l+yTZmWbwfcr2NySkuzmzZ5BMYf35czcZ2UXa+4SDgdcM1Y2OTJ+6+tLdMGZ077dvlRPJE0rGJopE0daWsQiydMG4kXqybM1njCcnBnNBAOmswCuaVw5HlafY8YBSh2A3W7wiHJNoUVG3ejrjZqmqqeUgoHDrXmFC3RpxtPmzwCF44cYNgbUNtnq+M4nkg6WotmNuMVFHE0g6dPkTMTlNyUUbMBt9eDVC8KTuQzGDV6XbX3UZ9KbVT0xknqppsgxquAJ/uzYbRGc9rjq3L6kGYLiNieWLJSqpkoJzNM2ceF9vzN5FOJWGtBUooWBm5OVFjtT2dIr2QjbyorJ4OR7A9gKCDo2b2qfSG9k/towqEgzhjSG8vW7c57nwXAiUf3wNvbD+b9GORfQRG0ZUqPl0ooALz9/Y4VVI1S+tw/rtius/KTPjWh9vVVmmI1RTdqMG7UZN5Js3K39M1tjRqkA8YFFcyaqWuBTH1DzHHmgtkMWzazoNXotQKAHtXB9kIyF44cgEdf2my6Fs1JU/Oh0xeb7tsGm0bLdo3rte2aBZlGzZy9aBjudeqWVVNqs7RON5+r7L6dTvbX7PGNXlM3tzXjNGXWrXyawRerqXX2cdOsKxJltC2r641kf5dkH6NWs5bafQFv03z18vnsmX3WzL5X/NjA3ayRNwM48jWzL8KgCO67YYzthzYWT7QPQLT/e712idybOLwvVm/d77t+aeV084QhuLdutOn1VoM0IJ2S+LGzh2Dpmh05P1Z29/WT7EF5MfY9FBT07OY8sPXyO0MfEOpnCozeN736hpjpbJ02MPTb+xwKCqacOTinMJDZ62k0wB0+4++GAWBQBOvmXNb+t9Hsi1WTeQFsB6ZmA+5CAjCrQMPpGrrs7du959mD3HyqNFvtm5vBvtFvd6EBj1dVp81OVrgNvLwI8rMZnbwxO4mSrwemjDXNAtG+t6yKfLl5nfL5DHkVGNc3xEzT4WtCAbzx3UsdP1YpmAVwTKEk39F/sM2+luxKbFstZmfwVn6FzIB2Vg8v32RZbtlukNam0lXAjH7I3Kbu9KgOGpbKLyW73m/5EqQHPG7OTOe77aguONNOJiWSqQ4DMX37FKsBjFXZeu39LVexADPJlMLi17a1VyW1Czhi8QSGTl/cYQ1Wvv3urII3/YzSvCVrceHIAZj/yuYOA+FQUDpUNvaq3LxV6qJR6qOTtEC7z4fWV/DO+Y3onWnv4TbYMUvLNEvfNBvsG/12O0m9Mxvwe1V12qqokNvPlVZSX5t9zm4671Z9Q8xwlj7ZphAOBXB0r+4Fn7iJRsKWheC09eFzrhltGvy46emYz/ozr94frWiRkeZkm21PUb9gAEe+4mb9j9N8ZadVKbUfwXAogMOtbbYlwIm8duf8RqzYuLvDTJw2eHRCv67LaPbZqUKDt3AogEPJtg4DDv0sw56Dh9GcNF6bdMKMxTjnhL54ddPeopx0KdXHWn9W2O4kkpPG3VaDlHz7GJbCnuYk5i1Z2+EMu91MoX4NltVsncZt5eGDLa3twVgsnsD8l3PTPJMphcdXbMKKjbtzgsHsSqYaJ7MKbpqNGw10jQbyCvazxNrzKyTrweg312ywb5Y6q10/dX5jznFh9tpZDfjt3vvs18UsTXra5BGm/WfdrG80WoOa3XTe6D5Wx809T642fW8TyTZX1bGtnr/GKlCqq42azq561dPRjFfrT+0CvkpZB8cAjsrGLO3CzQ+xkzMvTgY0WloHkF54z+CNykEhPRO3+LVtuPz04/DUqm2uBly9w6EOa6C0AZSXPRGdmHPN6QCQE0hqM1JPrdpmGsC1qcqfoc0eEDn5XkspZVgFUBvMWQVn8eYWDJu+GL3DIQTEvv+cES2l0E0AmF3cwkz2GXY3VYaNghOnA04jYpByZrYfy9btNj0WsyuZOplVqG+IOZpN1p6P1WxdNu3kSCEBvJMTPtmvtdlr7+Q7J/s1clNwRRvwW7332pr35e/uaX9eU84ajPHH9zUsXqQVlNLTz8TasToBre1v9szchBP6dDhZpa8Gqn1f2mUL6L8zshmtXwTybz8CWM+YFrM1hdOZWrvZcrvvOb9lMphhAEdlYfZj57bynpNSxnY/mPrcdKPGtESltqc5aZkCZiQUFLS0porSK88t/Y+lm9S2Qvgh7RMwXivkdEBgVAVQG+BanWXXnnc8kUTmBLvrmUaFdBl8J1kQAuCmCUMAwHFLFf0ZdrMWBlb7pqW46QehWhVLsx5xho/l4cdDX5HTLMiYvWh1h+biTjav/a65GUhGwiHH75+ZNqXa16CazZJm/+YWOuvrZOYln56xWtrigpWxDieytJkw7bWat2Qt7pzfaHoM9aiucjwbY3eiJvv7L6WU4QkCfUrtIw6/L83abMy+0jjV12iNrb5wllXlUbMZUyD3+94oNTLfmTQnM7XZJ4aMZsvtTiCVq+2BWwzgqCzMfuysziJlV4xzWsrYriS8SDp1zY89uKj8ImHrnnB+oJ1lNFubUErhUKD9325n1AtR7uBNANyfKQKQrXcBx5A2wF02fZKj97dNpauaVgWDOd+X146Lmqa2aSmJRoMksyIrE+c+5ypQ1A/EzVoYGMkuUpAdpJR6hlkvFk9Yvi/xRLL9vXe6n9rvmpvgSDKl4bPfPzfBbfdQoD0o7q77HGuMfnONZkXc0srQm6UXmp2R0I5Fs4IhVjN3ABwdQ3tdfG6LMXPj9sjWqii7Ka6T/XmKJ5IIBQR9akIdqn0DsGz9YXQC3ChAz6fdQPbMntF37bwlaw2DMqO+nwAwY+FrSGRlgnjd9qCYGMBRWVilXWSXddbOIgHuy9NafaE+MGVsztkiVqgkPZHC1oyUws0ThrSnBPlBItmGmfVNuLdutO9SUfJNL3RC39vNaB1iIbRBvNMUuWQbkGw78h3aozqIq8/oOBuhl93fLTudrb4hhqVrdrT/vWLj7rxOeDlpAp7NKIWtlCcGyslNqqk+ILYq4mUlkWxrf0+yB7YCGBbicBow2n0OzAqkTHt8leGsqXZcWM3K3GlRbMPpMeRmNsbqmA4FpCTZEW1KuSqDb1bBM9mmUFNd1d7WxUmKsNPUSLuZNKN9NNr2io27O5xYcpMWqe+7Wqy2B8XGAI7KwuzDpqUfmX2g3H6wrLZjtr6gkCCukPta9SMi7zmZWfNhl5UcDy/fhEdf3oyUD1InNVqaULmKaph9DtuU8/5nbgVF0rMFcHZm3w0BTBvlOnGwJYUnXjUv/21UBRE4soZRsm6TTxps9pltuzL/GqMUNqsTA5UwY26nw4yFRdNlPbNAQ3ucQsvsK6BDEJ+9DauAUd8jzG6NmH5QbfXdoT8ujE44WM0OD7SoOKknSB+nThrOA+azkTWhQEHBW/b3mdU4w23BFasKnvrXyCpFWHs9rFIjjQIls9L/M+ubOqwT7B4KGG47u5qvFavPR6UEbNly58iJSmDa5BEIh4IdLtN+4Otqo1g2fRLWz70cy6ZPKujDZbUdsy9wbb1FPrK/Bh3+9iIaCWPONaPz3m5NKICA040RAP/PrLnhp+BN88jyTRjarzxrCaxejcNFOkmilUe/58nVnp+IUTgyqNe+JwTOv18A5ymmiWQKMxa+hhkLm9oHRV4cXfqUvPqGGBasjDm6XzyRxMS5z2HY9MWYOPc51DfETAdj0UgYjbMuxgNTxub9XeoH2us+b8laxz2+rNK+6mqjaPPgRIKToCf7GNV+27SBshbIGYnFE7jp/17EnfMbbQfk2nGhnTTRaEGJVcPpaZNHOAp0FIAFK2OIxdMtjbQCI0N1x6Ldc39gylj06dGtoF5t5w7v2+Ex758yFg9MGZszttHPpGfvmxG7WUj9a2T23scTyfZtmY23Lhw5oP090V7HGQubDPdxZn0THl6+qcO6RbPvLjevaKWkRbrBRt5UNsWcus5ekCuCDrncVs2NtfUWhSwG13NSqU3fxFn7AnPDizStzoApsP4iUhmzmJVGy1QA4It1j07p1yY5XtsFZ59po2bYdu0KtJRabS10MWZm86E1KR82fbGj5x4Jh9A462LL23jR5F3/21jIb7eXDeezG2RbPXZENxbQ+uFZ/TbbfX8ZNUg3el2cvo9mKd52DeWNln84aRxutV9uXlf9urvs9bIXjhxgue42+3kNn/H3ooxlNrhIK/Ubs0beDOCo07FK4bBqQGp0u5n1TY6rrJnRyvha/WBlV8IsRtqZ3wYpVBkYhKVnuM3aHpRLKCCYd/0Ywz5lfqZVkyzmIeUkZU8TCgh6dq9yPKgvlQemjHUc6DppLF7fEHPcK8yIVqQHyH1NteAhYnKy1GhfvDg5qtGXyrerOO317LjVCd98TlgYEcByXZvdyWi39xMB7r9hrON0Vj39WMbufc5+XvmcvHbC7nXwOwZw1GW4+TKzOpNoVJI2XxvmXm4bmGn75/RsnRv6M2SstEnUOUhm5Oy/X3FrpZgpN5otclKRMQDAD6F6KCAIBSXnxIGby7MDu5v+78W8eyz2qA6iuSXlqqql1SxQsQbrZoqVpaIFIWa/70ER3Hj2YCxYab4G1Y5dAGI2ZrAL/MzGOKGgYN51YwDYnwAx4uSkNdDxeXlxPJg1KbebifQ7swCORUyo03HTJNKueajT4M2qwpTTAgTal10xgix9Xykip2pCAfTp0Y3HjU/58PyrrVKlOcfiCQybvjgnlc2OH4I3IF0FMPs3JSDAlLMGY+maHWjO+kwa3V7fAwsAXt6wJ699CQakfR2SmyBIX7I/uyprKZdsF7NAmF3PPq333LXjoli6Zkde36UXjhxguw92fdXMTlYb9WPUyu4DyOt107fOMJNd+fbRlza73o6ePq28UqtKusUAjjqdfJtEZrNbsK39EGlfHGapTArpdSrRSNiyD5M+0PNq5o+oEIlkG77vQZ8nSqsOClp8kJ7nVDgUyCknb8TtOrVS0RdMAIBIkSqQlkqbAua/vNnVb4M2GG8uIDW0kCJJ2X3ytN++UhwHgiO94r76WGNeLUT0s0lG68yc9OxLJFNYumYHlk2fhNrvPOP6GDSr/qkxOzkciydw6reeBoAOM7P6z4RZP8Zin7TTV4/0Yh1vLJ7APU+uxuWnH5dzXX1DDLMXrW4PKrW1htEKD/AKSqEUkd8B+AiA7Uqp0wyu7w3gYQBDkA4Wf6SU+r3d4zKFkgrhdA2cHSdr0bIfd6jNGV6tma7Zujp92o/+C6cSsaBI5xEKAClVvB5q5E9OPsORcAiHW9tsA/xyr6WMhEM4eLiVJ8Y6GbPjSp+eV98QKyhI0M8aapvKDgIA63RDLZ3RbozghNGax3zGDEERHBWuKulJjVKPC0KB9Htn9rGvhBTLoqyBE5HzARwA8EeTAO5uAL2VUt8QkQEA1gI4VinVYvW4DOCoUF5UuHT6pa9fXxZvbrEt123VjNcsZ93L9XhuCOBqzYPR/TlcIqo8Xn923RTlIHIjO0VSX5hmYCSM5pbWogYpWhBgV1DIy3V42jq1fAqNdHgcg3VjxeLXatl+L3JStCImIjIUwFMmAdwMAIMBfBHAUAD/BHCyUsoyJ4MBHPlFMRZaa2kdbqtGuamI6eUX5cThffNe+A4cOUM57fFGlKuQ380Thnj+PharITRRZxMKAD26VX6DbfKfaFapegEQCEjJe2MKgCoHLYO8FvGgcmqxT7QGJL2O0g/VXY3YFXspN7MArtiNvH8G4BQAWwE0AbjDLngj8pN760Z73hS2dyaVJ5s+n97I0jU7HH3JhkNBTDihTwF72NHLG/Zg4vC+CEp62XlQBBMzjUUB+2bC2lrC1jJ+8h8pQrUzBm/U1TktRJFsQ1mDt1IWzKDSGtovjAUrYx3W1pU6eNO2W44AJZ5IFrzdYu91myrPa+OU2/oIflHsIiaTATQCmARgOIB/isj/lFL7sm8oIrcBuA0AhgwZUuTdInJOq1TpRe+aUEAMz5bpc9rN0j/tiqoAR2a7tApSXkimFDbsSmDdnMsMr7dbKzgwEsa8JWvLmkrp35+O0ps4vC+Wv7vHl6ksVFkq5QiqlP0sVKgMM0Dlxu8yKpTViXM/K/YM3KcALFRp7wBYD2Ck0Q2VUg8qpcYrpcYPGGBdMpWoHOpqo5hzzWhEI2EI0D4jZScoAkE6uOrZvcrwB7amuqpDkBjLNCTVqkXVN8RszxL1qQlhazxRlHUmVo9nFVhqs4pc9+IfG3YlcN8NY7DBxykjnZHT7wuvhAKcd+pqelRXeZotUgrVwXT/unwxeCu+zvxNEg4FfF3AxEqxA7hNAC4CABE5BsAIAO8WeZtERVNXG8Wy6ZOwfu7luO+GMQiHgpa3FwD33TAG6+dejmXTJ5mW7NWCoHlL1ubM8Gm9dOzOEu1pTrYHfV5/4WotDoyYBZZBEcy5ZnT7/ckftLLNtd95pty70mWEQ0FHA02vPieRcAjzrh+DSDjk0SNSJdibSGLa5BG2v0t+MqBXd8y7bgxKfH6jIJW0r16oChQWZPtZ9wr6rGQrKIATkUcBvAhghIhsEZHPiMjtInJ75ibfBXCuiDQBeBbAN5RSOwvbZSJ/0M/ImVGZ22nMgh27ZqBb4wnU1UbRp8bZgEzB26BJAe1pmfUNMUyc+xyGTV+MiXOfw4UjB+QMGATAjWcPRl1ttOzpk35Xrp9FruErHadp1wrwZAblYEt6jW3jrIsdf2dQ5RsYCbf/LlVK8B6LJzB70eqKaUofCYe6Tk5uRrJNAZmet4L0rFVnsac52T6WMTtJ7VcFvQtKqRuVUscppUJKqUFKqd8qpX6llPpV5vqtSqmLlVKjlVKnKaUe9ma3ifxBm5EzG3RlX250djS7GagR7fJZV4xynBqlAMMfcat7W/3ox+IJzKxvyknxXLAyhjOG9O7wuArAgpUxzKxvYvqkjS42FiALkXDIk3LWyZTC7EWrMbO+iYF6FyFIfx+PvecZ3L3wtYqq+FlJ+3qwpRURFydFtJ/rSo95km3AhSMHYP3cy9HS2rl+tbKXq1SKCj+kiPzBLjDTZK+ji0bCHZpI2j1OXW0UPbs7qz0UjYTROOviDlU0rcoFh0NBzL5ylOUMwCPLNxmmeC5/d0/O4yaSKVfVH6tdpGiEQ0FMHN7X8e3JWqWcre/stNQsL2bh4omk560zqPy0345IONRhdlX7/o0nkmguV7+WLiCZUlAKjtJUtWbfABy30PHzoPzh5ZswdPpi3647LDSbRVuuUimKXYWSqEvQAjAnzcO1qpb5Po6TM+pmLQnMvna16pUADFsc2N3f7Avd6dd8j+qgbQN0/W3HDu5dUG86OkIAfGTMcVi6ZgdnS8tMWyN74cgBDL4oR1Ckw+9BfUMMd85vLGgWPxRwHlyYSafUSUEVmitJPJHExOF98cK63ZavfT7dDBh6O6MPjjUK6c/IhBP65D0+cFLt2y8YwBF5xCow8+pxnEzvZ7cksGt9IACWTZ9UUJuEQhqHC+A4eAOAltY2Bm8eUgCDBZ/QUqWXrtlR5j0hP0ophRkLmwCkfyfueXJ1wSnYhQZvAmDONacDAGYvWl1R6ZCFsAveurJIOFTU4+DmCUNMs3tSSuHVTXtdnRTWq6SecAzgiCqEFmBZeWDK2A7Bn1FVy2zaF5aT2wK5aZjhUBDXjotiwcpYXsGf2x/BZBmatBKVwoUjB6C+IcaZUDKlT/Pyw/pGBeDO+Y0YGAlj9pWjMHV+Y7l3qST4K2QsFJSiV+lcumYHwqGAaapwITPBF46snDZmfk63JSIduwArmqlApmeXDqBPtXSSOhAOBXHThCE5a/jurRtdUZXPqLR6VFduqeZSenj5pqIPgIMB6bAuliqP1u/TL7QiENMeX8Wqp11cMqWKfmIhFk8UbZ3ngpWxiilkwhk4ogrhpGF2toGRsOnZ/GjW+jqr2wIdUzONaC0DyplCM3F4X7y6aW+XWYtRKfJJZaHi6F4VwJ3zG9E7HEIoKEimOJdQLkER3HfDGNeph1rA5DfJtvTgPRgQpJgpQRVIm+GuhObenIEjqhB2DbONvnDMqlo+MGUslk2f1OE+dg1gDzk44+VmAXCfmhBunjDEsIec/v9O9agO4vrxQzpU+SzWjGAwkyNSSKZI52yLSn53sCUFhXQhhmRKtR+HwQrrTvzAlLEV34/qvhvGAAD2Hip/KqSXGLxRJauUQiacgSOqENMmj8gpMhIOBduDt/qGWIfqlReOHICla3YgkUy1FxnJnnXT01fANDq7m0imMHvRasszU3azeHoN374YADD++L45VTfN9sHKwZZUe0W2aCSM+6eMLdqMYEopy5YMTnCIQ36gHYd+LQ1uJBIOZVLKK7tmX1dZL0ZUSXpXyFIQBnBEFcKqxUB2BclYPNGhsmBKqfY0S6sATKuAOXT6YsPr44kk6htiho9R3xCzbEGgp19/Y1R18848BzbaEFRrylnMVMrKGe4SdR6hgOBgS2uXqXZIRKVVKckIDOCIKohZiwEnFSSd5nbXN8QsZ5eMHsNNCwIBctbrZc8eRmpCBS+E1s882jHqKVOoSDiE2VeOwp2PNaKCJjeIfEsAVFcFuKaSiibqIouEiqdPTQgtrW1l+az7obqrE5WdQE5EAJznbDu53bwlay1nl4wew00LgpsmDOkQAGrBXyyeaF+cf+BQK0LBwk+DaTOPZrT1gD++Yazl7dwKh4KYfeUoAEDv7pWRjtFZVcrZVLKnwII4VDzaEoNKWw/aGdVUVyEULE+IInDWc7fcGMARdQJOm086uZ1dkGf0GFb30bccuH/KWNxbN7rD9UbBX7JNoUd1Vft986W1OTArmZ5IpnDXY6tw5/xGdKsKoKaAoggB6bhNAJixsImpXmXG2U+izs+LViVa6n0p14N21nYehRYQi8UTZfvtVICv2nSYYQolUSdgVOAkm1mrgWxWhUjctiuIRsJYNn2S5fbMgr+9iSQaZ6ULnUyc+5zh40fCIfToVoVYPGHYYFxb81dXG8Ww6YsNZxa1H+t4IllgVUnBA1PSVeXyKcJCxREJhxhEE3VyXs3MlrIFjbac4KuPNXqewl9O2u9yJX/vVkIlSs7AEXUCdbXRDuXzo5EwbjZouO2kt4lZO4E+NSHX7QqcBox2l5s9/uwrR2HZ9EnYMPdy3J9pTmz2fJ3MPhbyG5pSClPnN+Kux1cxeCsTo+O2kgcRmm5V/Kmm0goGhE25i0whXYm0MwVvQPo7t9J/A51mNZWTKB/ml4wfP16tWLGi3LtB1GVlFxWxq16Z7320+5m1RwCOVN3sHQ5BBIg3J109vmZmfRMeWb7Jl9UjI+EQWlpTaC6wLHooIOjZvQrx5iR6h0M42NLaZRo1RzOtM/z6HheqGIV2iMyEAkCFd2mgMhKp3PR1AXD/lLG+aeYtIiuVUuOzL2cKJRHlMKt2aXcf4EjApeWQ2z2OWXsEAB0Cu3giiXAomNcXa31DDAtWxhwN7Avt7+aGvo/fxLnPobmAs5Y9qoP43tWjcwrEzF60uuizUKGglDVQDAWlvX9goXvh14FHOYI3AVBTHSxZ4ZBKTnfVTp5USgU7OwzeqBB+/A51wqjQml9xBo6IPGE1k2bUdsButs5s3ZuTdXXZzB4rWzgUxLXjonhq1TbPB5LaDNHSNTsMn7fZGj03tFkafcN2p889X31qQujfsxpvbz9YtG3YeSAT1HvxGlKuUp3UcNr2w2+0Qd+9daOL/nkjouKI5pHZUwqcgSOiojKqJmnUe86o6fiMhU0AOs7WmS0izmdxsdV9+tSEctIy760b3SHI7F1AiqNZEJvNqnjMxOF98cK63baDaG2WRv+aFnsx9qFkqqzBWzQSbn9trV7DriocCuBQsq2gAKxUIVUlBm9A+vVZumYHgPR63anzG8u6P0TlVokp325PDJcbAzgi8oTTgMtpoGc2GO8dDmHi3OdcrbWzGtjXVFeh4dsX51xulEaqBXWxeMJ0tkC/Ds1s/4xmII0qiWpn9peu2eF6EK29pnZBTaGzK4ky51o1t7SiviGGutoohvbzJoArZRptsQVEcO7wvli2bneRHj89UKvU2TOvbI0n2j/XRF1dpQVvANp/RyoFUyiJyBNOUx7N0twEwPq5l7f/bZSSGQoIIOiw3srJDFd9Q8z0rHj2dt1yW7zFadEW/WMVkhoYMShmogUoWspIJbU9MAqutNRXLwuYRCPh9tnXfYeSFTkgKYVIOITGWRf7ukhQKUTCIRxubStpGXoi8o7TbJlSM0uhZABHRJ5wugbOzdq27OCouaXVsEiAk3Vxtd95Ju/7eimftX1erKvpUR1Ec0uqQ5EYfYpoPhUrw6EguocCJSvccHNmNtLotfByBij7vTA6ts2EQ4Gyz0qWWk0ogGSb6jIVT7MJgEhNqNMUMKHK1KcmhFlXjEJdbdT0946saSek/MQsgGNzGSLyhFEvOqOzWW56xtXVRrFs+iSsn3s5lk2fhLjJD5KTdV6zrhiVd686L5ntayyewLDpizFx7nOob4h1uM7oNQsFBDUh51/hB1tSiNSEcOHIAZi9aDWmzm9ELJ6AQrrCZ8rh4DuQ6Xauvb+zrhjleB/0guK+bbpWAMaI0+DNbrtmx4S+F1ufmhBunjDE8D3pbtCLzgsCoDpYSKv54mlOtnXZ4A1IpzmbfTf5XY/q4hyvVFqRcAgN374YdbVR1DfEGLzlKZ5I5vz++hUDOCLyTHbAZZSK4DTQM+Kk6bfVvuW7XS9Z7avCkQIk+h8Ro32fd/0YvPHdS/FApoE5YB+c7GlO4uHlmwwrbFrNGekftU0dCXK0dYJuG/6GQ0Hcd8MYbJh7OW6eMMTx/bSZ2HzcPGEINsy9HG0WgZ7RMaHNvulfs0PJNow/vm/7ewKkX6Nkm/J04NStKtD+ft8/ZWyXDpL8KhIO4d660RXR+NfI964enXMigipLKCD4yJjjMHHucxg6fTGL6BSoUtaxMoWSiCqGm1YFfuU0HS/f1E6vy5ibFfTQ75+bFEN9mo/GzWty4cgBeHj5JjdPocP+uk1htbu9m+fuVigomHfdmPbXiiXqS8dJIRtt7aWW1luJxW/61IRw+enHVfRzsBMJh9CjW1Wn/ux0xvetXApdF+81plASUcXzyyxaIbKfg5l8y/9PmzzC8nHdMhsU6PdPe05WM4DRSBgPTBnbnuajp39NABjuvzbrp5Vrd0vbXzcpvABMB33a5UZVVZ0wS8PUS6ZUh7PBpU737cqqAoKQQcpqj+pg+3fPteOiWLAy1n4sOB1A+ykVdk9zEgtWxjBt8ghsmHs51jucFQ+Hgrh5wpCSDSLzyLhuF08ki95OpdwKDd602X5yltHjB2wjQEQVxai8f6m4rThpRv8czGZVtB8Rt9usq41ixcbdnlQE1AIqq/3TbxdA3jOkZvcHOs7a3ZlnepC2v9p2jF7TmfVNePSlzUgphaAIbjx7sGlxFC1YdTsw1Dd9BoDxx/e1THnKfnwRwIeJMx0ERTDhhD7YsCtRsbMeyTbVPnNj9tmbOPc5w+A9Ek6nFGtpt1qrBX2jYH1LknLLbuOy+LVtju6Tz0x4Pm6eMAR/fqmw77Ps+9aEAnn19SyHHtVBHGwpXnXTcCiAN797KQDns/zRSBhD+4WL1p6kXEJBqZgTZQzgiIgccNqA3C2j/m/ajFC+27y3bjTGH98XsxetNlzv5oR+Vsps/7JZBUdOmM1m1VRXFdSsW9Bx9sroJMDM+qYOA9KUUpYDVC2oc7I/+l5pN549uD140/bFKoDTB/IzFjb5PngD0q+N1ni+kOqgAVivzSy2vYmkZUU6s+A9nkh2mL1rU0cGhtpxp/3/rsdW+aJ/nv65+KkAhiAdUHrdxqNPj25otvjcThzet/0ERDnTE4/pVY3397cUdRv6qrlOGtHr10AXq9pluV7zHrrfGr9jCiURkQNWDcgLYZUWWsg262qjaJx1cXuRE+2xb54wpP1vs5THoEj7PrhNW3VSyMaMk2bwRimQTkyd32hY4VPz6EubXT2eNjvpZH+0wWdKKSxYGcvZB6vUpQtHDgCQf6pmPqz2JxwKYuLwvraPoQ2+CgpOpLxVEu1SqSImxXskq1clkP77nidXt/+tBeR+CN6A0qWNTRzet/2z48RNE4YUJUDQAjMzr27a255WetOEIXlVzfVCsYM3jfbd6KQoVSKZwtT5jRg6fTEOJ1OGqcaFKtenYm+eJzzLgTNwREQOOAku8mWWFup0m1ZpllYpp06LwpQqbdVsNks/uMxn5kK7VSyewNT5jfjmE0343tUdn6PVY2WfDdbPQGqP4XS2Ux+Aa+9Zd4vecQtWxjD++L4FHWduetNFbWYUtWPDizPvdqlhbQpFSx2zK2xh12KkviFmOtgzO5S016u+IeabmTcg97lGwqG8Z+7tvLx+D1IOptMCApxzQl88tco+nTMfdjPD+s/pgpUx37xXxRKLJ3Dn/Eas2Lgbs64Y5bgwU6WkoTpVKevfAM7AERE5UkgLg2JuUwvCtJ5uRm0IzPitKIw222R0eX1DDBPnPodh0xdj3pK1uPHswXmXPz/YksK0v67q8BpZnWHXD90EwLXjojkBbuOsix23U9DeI+09SyTbTH+MtYFkvseZZB5fP/tqddtl0yeZ3iYaCbc/by/6nhVzXY+dvYmk5expd5sei998oimvtD6/zLwFRQw/88XugZVsU47SYo/qHsKrm/baBpJuemFqwqGgo9d/azxR0plvt/SzmV7MgSkADy/fhHueXI1rx0VdzZR2BgLz3yA/YgBHROSA2+qFpdpmoamdhaQ8es2swuRTq7blBKkLVsY6DDK0AMzpoCO7wuONZw92dD9lsZ9GzeKNBEVy3jOrQe3WeMJx6qgA7YGkfuZQe82mTR5h+hr1DofaixhkDwizj7tKOlNtZGAmGDWrfrqnOWl6IqS+IZZX8BkOBWwDglIl6rUplfOZN+p5WC7xRNLTGSDtddWqhzpJidS+a/xqw65E+wkXL08H6CuTuu3x6YVQoDzpqgowTHH3q4ICOBH5nYhsF5HXTa6fJiKNmf9eF5GUiNgnzxMR+Uw5ZqucbLOYqZ2lZlUUwihIXbpmB5ZNn4QNcy/HujmXYUNmQOo0iNNv79660bhZt9bFaoBntp/Z71ckHMpZHxIKuC/qoQ827CikG433qQnlDOq0wN7sLHM8kexQEl8/6M0+7tysRfRbo+jsFFizQbC21id77WS+614Pt7ZZBgThUBA36WZJizmM1YL1YdMXtz8/P882FUp7bw8ebsX8VzYXfQY0ZNKGIlsh77H2PeTku17bTiQcgpNJy0QyhbseW1WygjYBQftv3Lzrx+DmCUPK0tbAi3XtpVLoGriHAPwMwB+NrlRKzQMwDwBE5AoAdyqlOlfNUSLqMsrRwsBum07WjVUKtxUmzQYuRpU9zband2/d6A4VIu1aPBjJfr/06xN7h0M42NJqet9IOITDrW0d9js7rcdJVcdEMmX63LfGE4576SmYNzjXVxw1eo202T+tdL5fSuYbNZIHrAfB2dVf8z050qbM20DoCwdpirVWLhQQHGxpbZ9p056fX4K3cCiIgBQnxdZsdlGQLkrjRcAiAKaclZ7Rt2rnojVRX7Ayltdrr30P2X1vBkVw3w1j2ttXOG3FUso03+w2G0A680J7v0IBoFTL7Srl5GdBM3BKqf8CcBqQ3Qjg0UK2R0REHZUjtbNYpk0ekZM+EwqIaRqPWSCVnRpnxEm/Hy9eW32Kao9uVTnVCfVEgDOG9O5w5llL65lZ3+TJ2qlITcjVAMXqtvrZq2z64K+uNup5g3lNOBQ0PT4i4VCH2WuzRvKA/QkPbUZi2PTFCBRQkVCp3BnJcCjYPsDWq6uNOk7tzWa2i0ER9OyeexwmkinHlRbdVGSsCQVcr1Obc81ohIKlX+HT8O2LPVtLtnTNDixds8MytbGmugrjj+9r+11lRP89ZDUbHg4FcePZgzFvyVoMnb4YU+c3eppu2a0qAK8yHrUiU6d+62lM++uqDsF2KWulVMrJz5J8QkSkBsAlABaUYntERF2F3wqRFCx7MCDA5acf5zqQ0oKLDXMvxwNTxrY3VwbSZ77nXZc7YDZ6DC9fW7vAaU9zEssy/dP0EskUHn1psyczJEq5G6DY3ba+IWZ69l//fOtqo7hpwhDH241GwqaDWtGlW825ZrTh2sNwKIjZV45yvL7TSUpoSikoFD4zoR+wa+sh5y1Za7j2xulsqV44FDSthNmmlGkBmpRStq9BJByyff59akJ4YMpYbJh7Od747qXo06Obo/0GjhTKKXU5d/1slhe2xhO2n3f9zK7V2lRNn5qQ4fdQXW0U146LGn114owhvbFgpflntBDhUABr770U55zg7cqo5mSb5YmuQlmdT2Aj71xXAFhmlT4pIrcBuA0Ahgxx/iVPRNTVlSO1sxjmLVlr2D9r6ZodmHPN6LwbhBfy+nj52ubThFxjNWgOh4KOg7u9iSRmX+msTLiWvjlx7nOGr3t9QwzTHl9len+jFFXAOq1MszWewP1Txjpqc6HJ9/gA7FNC3XCSjped5quVcZ86v7FDKpmT2VKtJYL+uZs9D+09MbrOLt01FBDMvnKU7Wt0KGu6xOmMr/6kTD6flZsnDMH8lzcj6bI8qD5N2Wn6tXYc5vM66yWSKdxpMysmSPfD06d369U3xPDoS5tzHkMBWP7uHtcnHJykaQPp97m+IYZXN+119fhA+Rp1BwSZ6WnjrVdSI29RBZ5JEpGhAJ5SSp1mcZsnADyulPqzk8ccP368WrFiRUH7RURElWXY9MWGP6sCYP3cy0u9OwXL7s934cgBea93MRtU6QfdW+MJBGwGX/r+Z9pjRjJr8/TBswA4d3hfvLppb4f9DQXSKXjx5qTltgTA/VPGGg6G9K+L2WNo6ZdWPQ6Lxag/ohvaezLVZK1R1MHg3i5A0ISCYjibbNXjEYCjwNjstdcCd6tASb920mwtKXBkLG10csBoH83WxmUfL1avWQC5VV/1z1//GEZDfW3t2tI1Owxvo3+dzY4BN/TrNr38TjGiHbt2QaV2W8B5pc4+mZMa2veO00DRS92qAjjcap6P6cffGhFZqZQan3N5sQM4EekNYD2AwUqpg04ekwEcEZG9cgxui8lsoGdWSMPPzAag146LYumaHdiaaYnghHa/7IGa2aDbLPgIZUq96QM1/WAz+1gqdDZqg4OBkNNm8qXmJMg0KjrjZPCupbo5GSBbzQiZFWQxeg7aewrkNp3Pfhyr7xW74Eb/HLWBsFXAZxaAWu2/2esRFMGNZw/OmanKfpzmllbD2VGj7xmn+5BdtEd7Pl40vAfMvwPsuAmS9J+7mfVNljPl2m2dBHqaBwxm1P3Gj781RQngRORRABcA6A/gfQCzAIQAQCn1q8xtPgngEqXUR50+LgM4IiJrfh34FqIzPScnwajV4C4ogjalcmY+rAJ2/eA6+yx31OXAFTCfEXXCzUCoXLNsTrdpN5tl9DhWs05OZy60IMir18du5ixqMqOjf67T/rrK0fqkPjUhNHz74g7bvvOxRsO1eW4HzfUNsZwgVO9mi3RDABg6fbHpdRt0QafZa+7mRFOhs7l6bmes7II+/Wy60XE1s74Jj76UbrkgAGqqg2huSTk+zvWsjvlypVNm8+tvjVkAV9AaOKXUjQ5u8xDS7QaIiMgjVg28/fYD5JR+HVKlzyo66c8364pR+OpjjcgeT5vNSlitx8seKGoFKfQDkmEmA1ezfc13zZ7bQgCFrjPMbtUgAtNBqXb77PVn+jYBRvsHmB+XRvexWvdlN5Ok0dZRebUOc96StZZpj7F4wnDWRfteac5Ks7WSHWfU1UZNy9fnU7b94GHzdhyPvrTZMoAzC4S06pp2x4eb3pt1tVGs2LjbMBCyS3fO5ua2+rYU63ccwLJ1HUtQaK0O9K9TfUOsfb2r1vJE26ZCutS/Pi26viFm+T5otLWNVu0L+uTRwiGqmxF1MitsxW42249KVcSEiIg81JkaeOt1loIsZsFPQAT1DbEOz1M/m5DvQMJJQO+2Z6DTgg76QVOpB0LZg239rIxZYJbPyQ+3x6XZax0Jh3L2xWwdlV0Q7HZmzsl3g9ng103KLwDDKpJe9ay858nVloGoXaBjdr12ud3x4eZ51DfEsGBlzDAQAuyDeD03M3AppTB70WrcvfA1NBvU4NdaHej30+xzpNE3ubaaAc2mBZJmKdkBkbyCN/1sp5vZQKOiP5X4m8MAjoioAvmxgXclrckr9r6aBT8ppToEFV4FrE4CeqN9MgoUsme0uocCiDcn28/KG62hK9f7bDTY1jMKzOxeKy+ODbPXevaVo9r/1r/3brfpdhYRKKwKqtv7Gn0POT3+rNQ3xGwH+3Z96qImz0VL87M7Ptw8D6tgUAtAnARD+ayBs3tM/fO0+xxp3DZ919pCANbfiW5nzozeIycnKLTPoF9/l9xgAEdEVIG8GAx5KZ8BZbmUYl+1x7nrsVU5Z82LkerqJKB3kqJqdCY+HAq2p075LUh3MmjLvo3Va+X02HCatpldMXDekrW4c36jYRqmm9cxn1nEaZNH2FaPBHLXJGnfK05nXcy+h7xIkdZmgKzYNT+3++60+yy5eR5mx6f2+NrMlNHrarQOdvzxfT1pdaF/Plb7abRPToO37OPA6jvRbdqj0QkCs/dNfzx3d9lU3s8YwBERVSC/rRerpDV5pdpXr9f9WHEa0NsFCnavjd9SXJ3MDGUP9qxeKyfHhpu0TbP7ODlpYBUs55NCrd13xsLXkDBIq9NeB32l1OztZgeAoYBgylmDTW9vtA/5PF8nzw8AJg7va7n+TdsHwPy708lnyennwCqo0FKpzZ5Tm1I5Je31251Z34SHl2+y3QcjAnR4Pk5nWO1SOI2CTj2r70Qg3Rxcf2wazcyZrbE1et+yK+/uaU769sSiWwzgiIgqlJ8G05W0Js/JvhYy0+SkBL3Xqa5eBfSV9D4C9uv0zIJYwPi1chJw55O26fakgV3Al28KtfadYVSxNLsEvrYf+llDN8GaG04DXNOASID7bzDuO2jE6rvTy5NjZj3VVObxC3kv760b3T4j56QHpEZrDK5/PheOHOAoGLRah+c0ndrs+ZpV8XS6RtjofTOqvOvXE4tuMYAjIqKC+XFNnhm7fS0kxdKoGmS2YqW6ehHQV9L7COQO2pxUodTuZ3S5k+efT9qm28DYLuArNIXaybFi9DlYsDJWlDWPTgNcs+ft9T55dXKsrjZq2g8wnzV1VvvppGWBWQCkL2hiRatum70NN8WL3Dxft+9D9u3dVt6tJAzgiIioYH5bk2fFbl8LSbE0m52xSy3yi2K/j8VYQ+flTLST559P2qbbwNgu4CtFCvU9T64uWVq00wDXb6njTpgVTclnTZ2VfE9m1DfEHK+p02Zps2dva6qdhxOlfA8r7YSUGwzgiIioYOUeWLkJDOz2tZA0QjfrWfyomO9jJRS6cfL880nbdBsYOy1Kk53uqPXxKvR9s6r2WIzZCzcD7XKmjudzAsKLNXVOt+v2tdE+k05o+3xkLWX+n+VSvYeVdGLRLQZwRETkiXINrPIJDKz2tZCztp3hjG+x3sdiF4/xanbP7vnnM9PhNjB2O/D0Oji2qvZYjGO5Egba+b7GhZ4UKeaJD6v1nKGAoGf3KtPqqpVQtKrcJxaLiQEcERFVNK8HE4UMJithIFouxSyQUurZvXyCXDf3cTvw9PozYPWeFGv9JuDvgXYhr3EhJ0WKGSxZvc/zrh9j+viVVOyonDO2xcQAjoiIKprXg4lCBpOVMBAtl2LOTlbKjIAbbgaeXn8GzN6rSDhUtNfT7wPtcgUtxdyuVUVIq/fCi8+y33pKVhoGcEREVNGKERgUMpj0+0BUr5SDqGLOTlbSjEAxeP0ZMHuvZl85Ku99rHTlSo8u5nbz/UwW+lmuhPWwftd5WpITEVGXNG3yCIRDwQ6XMW3RnjaIisUTUDgyiKpviBVle3W1Ucy5ZjSikTAE6bP8XpV/NxvMVtLaw0J4/Rko5ntVqcr1PVPs7XarOhIK9KkJOXqfCz0+rGbMyRnOwBERUUVj2mJ+ypF2WKzZya6+9rAYn4FKmkkuhXJ9zxRru0Z94w4l21ztV7770NVnzL0gykHX9lIbP368WrFiRbl3g4iIqNMaNn0xjEYAAlREy4NsXFNTPnztK8/Euc+Zrn9bNn1Sp912pRGRlUqp8dmXcwaOiIioC+oMLQ/0OGNUHlzPVJnKOQvW1WfMvcA1cERERF0Q1w6SF7ieyb+05u7Dpi/GxLnPdVjfWs51o1xjWTjOwBEREXVBXDtYOfycosj1TP5kNzNa7lkwzpgXhgEcERFRF8VBlP/5PUWxs6XidhZ2RYp4AqeyMYAjIiIi8im/Nykv90wOGXMyM8oTOJWLa+CIiIiIfMrvKYpcz+RPXb03YmfHGTgiIiIin6qEFEXO5PgPZ0Y7N87AEREREfkUq4VSPjgz2rlxBo6IiIjIp1hsgvLFmdHOiwEcERERkY9xIE5EekyhJCIiIiIiqhAM4IiIiIiIiCoEAzgiIiIiIqIKwQCOiIiIiIioQjCAIyIiIiIiqhAM4IiIiIiIiCoEAzgiIiIiIqIKwQCOiIiIiIioQohSqtz7kENEdgDYWO79MNAfwM5y7wR1CTzWqFR4rFEp8XijUuGxRqVSzGPteKXUgOwLfRnA+ZWIrFBKjS/3flDnx2ONSoXHGpUSjzcqFR5rVCrlONaYQklERERERFQhGMARERERERFVCAZw7jxY7h2gLoPHGpUKjzUqJR5vVCo81qhUSn6scQ0cERERERFRheAMHBERERERUYVgAEdERERERFQhGMA5ICKXiMhaEXlHRKaXe3+o8ojI70Rku4i8rrusr4j8U0Tezvy/j+66GZnjba2ITNZdPk5EmjLX/VREpNTPhfxNRAaLyFIReVNEVovIHZnLebyR50Sku4i8LCKrMsfbPZnLebxRUYhIUEQaROSpzN881shzIrIhc4w0isiKzGW+OdYYwNkQkSCAnwO4FMCpAG4UkVPLu1dUgR4CcEnWZdMBPKuUOgnAs5m/kTm+PgpgVOY+v8gchwDwSwC3ATgp81/2YxK1ArhLKXUKgAkAvpg5pni8UTEcBjBJKTUGwFgAl4jIBPB4o+K5A8Cbur95rFGxXKiUGqvr8eabY40BnL2zALyjlHpXKdUC4C8ArirzPlGFUUr9F8DurIuvAvCHzL//AKBOd/lflFKHlVLrAbwD4CwROQ7AUUqpF1W6+tAfdfchAgAopbYppV7N/Hs/0gOdKHi8URGotAOZP0OZ/xR4vFERiMggAJcD+I3uYh5rVCq+OdYYwNmLAtis+3tL5jKiQh2jlNoGpAfdAI7OXG52zEUz/86+nMiQiAwFUAvgJfB4oyLJpLQ1AtgO4J9KKR5vVCwPAPg6gDbdZTzWqBgUgGdEZKWI3Ja5zDfHWpUXD9LJGeWqsvcCFZPZMcdjkRwTkZ4AFgCYqpTaZ5F2z+ONCqKUSgEYKyIRAE+IyGkWN+fxRnkRkY8A2K6UWikiFzi5i8FlPNbIqYlKqa0icjSAf4rIGovblvxY4wycvS0ABuv+HgRga5n2hTqX9zPT68j8f3vmcrNjbkvm39mXE3UgIiGkg7dHlFILMxfzeKOiUkrFAfwb6TUePN7IaxMBXCkiG5BezjJJRB4GjzUqAqXU1sz/twN4AuklVb451hjA2XsFwEkiMkxEqpFepLiozPtEncMiAJ/I/PsTAP6mu/yjItJNRIYhvej15cx0/X4RmZCpYvRx3X2IAACZY+O3AN5USv1YdxWPN/KciAzIzLxBRMIAPgRgDXi8kceUUjOUUoOUUkORHos9p5S6GTzWyGMi0kNEemn/BnAxgNfho2ONKZQ2lFKtIvIlAEsABAH8Tim1usy7RRVGRB4FcAGA/iKyBcAsAHMBPCYinwGwCcD1AKCUWi0ijwF4A+mKgl/MpCgBwOeRrmgZBvB05j8ivYkAbgHQlFmXBAB3g8cbFcdxAP6QqbgWAPCYUuopEXkRPN6oNPjdRl47Bul0cCAdK/1ZKfUPEXkFPjnWJF0UhYiIiIiIiPyOKZREREREREQVggEcERERERFRhWAAR0REREREVCEYwBEREREREVUIBnBEREREREQVggEcERFVPBE5kPn/UBH5mMePfXfW3y94+fhERERuMIAjIqLOZCgAVwFcpoeZlQ4BnFLqXJf7RERE5BkGcERE1JnMBXCeiDSKyJ0iEhSReSLyioi8JiKfAwARuUBElorInwE0ZS6rF5GVIrJaRG7LXDYXQDjzeI9kLtNm+yTz2K+LSJOITNE99r9F5K8iskZEHpFMR1giIqJCVZV7B4iIiDw0HcDXlFIfAYBMILZXKXWmiHQDsExEnsnc9iwApyml1mf+/rRSareIhAG8IiILlFLTReRLSqmxBtu6BsBYAGMA9M/c57+Z62oBjAKwFcAyABMBPO/1kyUioq6HM3BERNSZXQzg4yLSCOAlAP0AnJS57mVd8AYAXxGRVQCWAxisu52ZDwB4VCmVUkq9D+A/AM7UPfYWpVQbgEakUzuJiIgKxhk4IiLqzATAl5VSSzpcKHIBgINZf38IwDlKqWYR+TeA7g4e28xh3b9T4O8tERF5hDNwRETUmewH0Ev39xIAnxeREACIyMki0sPgfr0B7MkEbyMBTNBdl9Tun+W/AKZk1tkNAHA+gJc9eRZEREQmeEaQiIg6k9cAtGZSIR8C8BOk0xdfzRQS2QGgzuB+/wBwu4i8BmAt0mmUmgcBvCYiryqlbtJd/gSAcwCsAqAAfF0p9V4mACQiIioKUUqVex+IiIiIiIjIAaZQEhERERERVQgGcERERERERBWCARwREREREVGFYABHRERERERUIRjAERERERERVQgGcERERERERBWCARwREREREVGFYABHRERERERUIRjAERERERERVQgGcERERERERBWCARwREREREVGFYABHRERERERUIRjAERERERERVQgGcERERERERBWCARwREVUcEfm3iOwRkW7l3hciIqJSYgBHREQVRUSGAjgPgAJwZQm3W1WqbREREZlhAEdERJXm4wCWA3gIwCe0C0VksIgsFJEdIrJLRH6mu+6zIvKmiOwXkTdE5IzM5UpETtTd7iERuTfz7wtEZIuIfENE3gPwexHpIyJPZbaxJ/PvQbr79xWR34vI1sz19ZnLXxeRK3S3C4nIThEZW6TXiIiIOikGcEREVGk+DuCRzH+TReQYEQkCeArARgBDAUQB/AUAROR6ALMz9zsK6Vm7XQ63dSyAvgCOB3Ab0r+bv8/8PQRAAsDPdLf/E4AaAKMAHA3g/szlfwRws+52lwHYppRqdLgfREREAABRSpV7H4iIiBwRkQ8AWArgOKXUThFZA+DXSM/ILcpc3pp1nyUA/q6U+onB4ykAJyml3sn8/RCALUqpmSJyAYBnAByllDpksj9jASxVSvURkeMAxAD0U0rtybrdQABrAUSVUvtE5K8AXlZK/TDPl4KIiLoozsAREVEl+QSAZ5RSOzN//zlz2WAAG7ODt4zBANblub0d+uBNRGpE5NcislFE9gH4L4BIZgZwMIDd2cEbACiltgJYBuBaEYkAuBTpGUQiIiJXuCCbiIgqgoiEAdwAIJhZkwYA3QBEALwPYIiIVBkEcZsBDDd52GakUx41xwLYovs7O03lLgAjAJytlHovMwPXAEAy2+krIhGlVNxgW38AcCvSv70vKqViJvtERERkijNwRERUKeoApACcCmBs5r9TAPwvc902AHNFpIeIdBeRiZn7/QbA10RknKSdKCLHZ65rBPAxEQmKyCUAPmizD72QXvcWF5G+AGZpVyiltgF4GsAvMsVOQiJyvu6+9QDOAHAH0mviiIiIXGMAR0REleITAH6vlNqklHpP+w/pIiI3ArgCwIkANiE9izYFAJRSjwP4HtLplvuRDqT6Zh7zjsz94gBuylxn5QEAYQA7kV5394+s628BkASwBsB2AFO1K5RSCQALAAwDsND50yYiIjqCRUyIiIhKRES+DeBkpdTNtjcmIiIywDVwREREJZBJufwM0rN0REREeWEKJRERUZGJyGeRLnLytFLqv+XeHyIiqlxMoSQiIiIiIqoQnIEjIiIiIiKqEL5cA9e/f381dOjQcu8GERERERFRWaxcuXKnUmpA9uW+DOCGDh2KFStWlHs3iIiIiIiIykJENhpdzhRKIiIiIiKiCsEAjoiIiIiIqEIwgCMiIiIiIqoQDOCIiIiIiIgqBAM4IiIiIiKiCsEAjoiIiIiIqEIwgCMiIiIiIqoQDOCIiIiIiIgqhKMATkQuEZG1IvKOiEw3uP4CEdkrIo2Z/77t9L5ERERERETkTJXdDUQkCODnAD4MYAuAV0RkkVLqjayb/k8p9ZE870tEREREREQ2nMzAnQXgHaXUu0qpFgB/AXCVw8cv5L5ERERERESkYzsDByAKYLPu7y0Azja43TkisgrAVgBfU0qtdnHfinDBBRfkXHbDDTfgC1/4Apqbm3HZZZflXP/JT34Sn/zkJ7Fz505cd911Odd//vOfx5QpU7B582bccsstOdffdddduOKKK7B27Vp87nOfy7l+5syZ+NCHPoTGxkZMnTo15/rvf//7OPfcc/HCCy/g7rvvzrn+gQcewNixY/Gvf/0L9957b871v/71rzFixAg8+eSTuO+++3Ku/9Of/oTBgwdj/vz5+OUvf5lz/V//+lf0798fDz30EB566KGc6//+97+jpqYGv/jFL/DYY4/lXP/vf/8bAPCjH/0ITz31VIfrwuEwnn76aQDAd7/7XTz77LMdru/Xrx8WLFgAAJgxYwZefPHFDtcPGjQIDz/8MABg6tSpaGxs7HD9ySefjAcffBAAcNttt+Gtt97qcP3YsWPxwAMPAABuvvlmbNmypcP155xzDubMmQMAuPbaa7Fr164O11900UX41re+BQC49NJLkUgkOlz/kY98BF/72tcA8NjjscdjT4/HHo89Hns89njsdcRjz5tjr1I4CeDE4DKV9ferAI5XSh0QkcsA1AM4yeF90xsRuQ3AbQAwZMgQB7tFRERERETUtYhShvHUkRuInANgtlJqcubvGQCglJpjcZ8NAMYjHcS5ui8AjB8/Xq1YscL5syAiIiIiIupERGSlUmp89uVO1sC9AuAkERkmItUAPgpgUdaDHysikvn3WZnH3eXkvkREREREROSMbQqlUqpVRL4EYAmAIIDfKaVWi8jtmet/BeA6AJ8XkVYACQAfVempPcP7Fum5EBERERERdWq2KZTlwBRKIiIiIiLqygpJoSQiIiIiIiIfYABHRERERERUIRjAERERERERVQgGcERERERERBWCARwREREREVGFYABHRERERERUIRjAERERERERVQgGcERERERERBWCARwREREREVGFYABHRERERERUIRjAERERERERVQgGcERERERERBWCARwREREREVGFYABHRERERERUIRjAERERERERVQgGcERERERERBWCARwREREREVGFYABHRERERERUIRjAERERERERVQgGcERERERERBWiqtw7QERERETUWdQ3xDBvyVpsjScwMBLGtMkjUFcbLfduUSfCAI6IiIiIyAP1DTHMWNiERDIFAIjFE5ixsAkAGMSRZ5hCSURERETkgXlL1rYHb5pEMoV5S9aWaY+oM2IAR0RERETkga3xhKvLifLBAI6IiIiIyAMDI2FXlxPlw1EAJyKXiMhaEXlHRKZb3O5MEUmJyHW6yzaISJOINIrICi92moiIiIjIb26aMCTnsqqAYNrkEWXYG+qsbIuYiEgQwM8BfBjAFgCviMgipdQbBrf7AYAlBg9zoVJqpwf7S0RERETkS+/uOIhQQNC/Vze8t/cQuoUCSLUpnHtiv3LvGnUiTmbgzgLwjlLqXaVUC4C/ALjK4HZfBrAAwHYP94+IiIiIyPfe33cIf2uM4WNnD8GLMy7C+rmX4+k7zodSwE/+9Xa5d486EScBXBTAZt3fWzKXtRORKICrAfzK4P4KwDMislJEbjPbiIjcJiIrRGTFjh07HOwWEREREZE//OGFDUi1KXz6A8PaLxvWvwduOnsI/vLKZryz/UAZ9446EycBnBhcprL+fgDAN5RSKYPbTlRKnQHgUgBfFJHzjTailHpQKTVeKTV+wIABDnaLiIiIiKj8Dh5uxcPLN+KS047F8f16dLjuKxedhHAoiB/8Y02Z9o46GycB3BYAg3V/DwKwNes24wH8RUQ2ALgOwC9EpA4AlFJbM//fDuAJpFMyiYiIiIg6hcdWbMa+Q6249bwTcq7r17MbPn/BcPzzjffx8vrdZdg76mycBHCvADhJRIaJSDWAjwJYpL+BUmqYUmqoUmoogL8C+IJSql5EeohILwAQkR4ALgbwuqfPgIiIiIioTFpTbfjdsvUYf3wfnDGkj+FtPj1xGI45qhu+//c3oVR2IhuVzWuPAfefBsyOpP//2mPl3iNHbAM4pVQrgC8hXV3yTQCPKaVWi8jtInK7zd2PAfC8iKwC8DKAxUqpfxS600REREREfrBk9fvYvDthOPumCVcHcdeHR6Bxcxx/b3qvhHtHpl57DHjyK8DezQBU+v9PfqUigjjx41mA8ePHqxUr2DKOiIiIiPxLKYW6X7yAvc0tePauCxAMGJWOSEu1KVz2k//hUGsK/7zzg6iuctSOufN77THg2e8Ae7cAvQcBF30bOP2G4m/3/tMywVuW3oOBO/2RMCgiK5VS47Mv55FDRERERJSHFRv3YNXmOD5z3gmWwRsABAOC6ZeNxMZdzXjkpY0l2sMSyTcVsZSzYIk4sO454D8/BB65wTh4A9KBpM/ZNvImIiIiIg+Ua6ahUpXz9XK47Qf/+y761IRw3RmDHD3sBScPwMQT++Gnz76Na8cNwlHdQ17vef7yfb21ICyZSP+tBWHAkfsrBbQeAlqageTBI/9fcveR+2mSCeBfs4HR1wNiHRSb7nOqFdjxJrDlFWDLyvT/d649cr8BI4FQj/Q+ZOvt7L0sJ6ZQEhERERVb9iAXAEJh4IqfMogzUs7Xy+G2391xABf9+D/48oUn4qsXj3D88K/H9uIj/+95fP6C4fjGJSNzt51v0Frofe2es1LAob3AwZ3AwR2Z/7YD/7oHOLwv9zEDQaCm/5FgTbU525f2+4eAHv2BHgN0/2X+7nk0sP1N4OVfA62HdfepAvqcAOzbAiSb05fV9AOi44FBZwKDxgEDzwDCkYr4TJqlUDKAIyIiInIq30Hyj0elB5XZnK63qYDZKE/uqxTQchD42Xhg/7bc63sPAu5c7e2229qA+EZg+xvp//53X+6skMG2v/lEEx5fuQXLvjEJA3p1c7ZPGVP/0oCnX38PS792AQZGwkf2N9+AwmkAlmpJv77J5o6zYY99HGjemfu4Vd2BASOOBG2pFsfPUQGQMz4BVPcAQjXp/7f/uyY9A7boS+nHzdY9Aoz/tC5QzPx3YAfQavDe6AWrgXGfOhKw9RlmPpPn81lxBnBEREREhbAaJJ92LbBvazoQ2LMB2LOx478PWFQe7D8C6DMU6HM8EDm+47+7H+W/2aiqMHDpD4BRV1vfd/UTwNPf6DjgDnYDam8B+p2gG5jvBA5sPxIk2A3Q+51k/Fr1GZqeWTHb71AY+PB3gX4npmdvtq/O/H+NcSqdkdE3ACMuxe7jzsM596/A1bVRzL32dGf31dmypxmTfvQfXDl2IH50/Zh0cPXjU4yD1qruwNAPWD/ghufTKYrZAlXpGauWZqDlAKBSrvcVJ00+MvvV8+iOM2E9BuC9+ybiWOQGf+9hAI6d/Y71Y7s9trUA/+AO4Ke1SIeJ2QSYHXfzDH2LARwRERFRIcyq1gWqAAjQljxymQSAo6LpoCJyPPDmIuM0s+pewAkfPBLwZd8m3Bdo2Q+kkrn3dTEb9cqiX2Pwq/NwtNqB7TIAm8+YhjOv/FzuDdvagL2bgPf1s1HNjrbhmhZc9OgP9MgKDJb9BEgYNL3u1gs44cJMcLwROBTveH333unXe+fb9oFgjwHA0acAR49K//+YUenZpl+cY/w+h2rSwUXzLqSkCstaT8EpH7weA8bVpYNIjZNZncQePPpEPd5743+4ddhu9NrZCCT2mO9rdJz1c4mtNL+u9haTWTDdbNiCz6bTIbPZzBBv33cI9869B3NDv0GNHJmda1bVmJ68Fcd94OM498T+OHNoH9RUm5TeyHcWrAKqSBaKARwRERFRPpp3A+/8C1j4WfPbfODOzCxQZiboqEFAVfWR652muCX2pGfttABlzwZg5e/Nt3vUIOPZuz5DgZ7HACJ4ZdGvcdrKmQjrBtgJVY21p38DY2vHHwnWtr8J7FiTnqlx4uLvWV//zDdNrhDgG+vTaXJWqW1OZmYS8SOvlX7G851/mu/XxxcBR58K9BzgftunXYvDG5bj0T/9GhdXNWBgclP6+mNOA0ZcCgRDwPP35973vK+lZwe3rEj/t+ttAEAbBLHQ8Rh82nnAmqeMgzgnAUmhwYzLmbA9B1vwq/+swx9e3IBDyTZcGXgeX696DANlF7aqfvhh6w34h5wPBYVkSiEUFNQO7oNzT+yHiSf2x5hBkfY2CvUNMcxbshZb4wkMjIQxbfII1NVGPd/nSsQAjoiIiPzD52tPsGsdsPbp9H+bXkynnknAuBBDsdexmQ3Oux0FjLjsSMCXnX5X1R2IDMGhnRvQHTZrl2r6pYOaY0YdmZUaMAL45bn5BwZeBBX5HiNF3PafX9qEu59owqOfnYBzeu8B3noaWPsPYNML9oU6egxIr82KjgMGnYk/bOyDWUs240+fOQvnJZYWdw1cAc9Zs/9QEr99fj1+87/1ONjSiqvHRnHqwKNw3zNvIZE8kp4ZDgUx55rRmDzqWLyyYTeWrduJF9ftQlNsL5QCaqqDOHNoX/QOV2HJ6vdxuLUt576Ogzg/f48UiAEcERER+UO5z5wbDfpOuzZdanzt39NB28630rc95jTg5EvSgdKud4Cn7ij9fjt9vZIJIL5ZNxOV/k+9+ZThRJdSgHzib+lgLZ/ZqFIEFfkq0rbb2hQ+9OP/oEe3Kiz60kSI/oVt3g38cJj5ne94DYgM6TDreLg1hYvu+w96dQ9h8Zc/gMDrj5enCqWNQ8kU/vjiBvzy3+uwpzmJS0Ydi69efDJOPqYXAOezaHubk3jx3V14cd1OLFu3C+9sN57tjUbCWDZ9kif7XskYwBEREZE/mM2OHDUI+KrHFQaN7pc9sJdgerYqeTC9LmvoB9IB28mXdFzbVMh2C5Xndlta27DjuychKnkWmShg2wXft1BF2Pa/3ngft/5xBX7y0bG4aqzBDFEeM39/a4zhjr804sc3jME1DvvJlUpLaxvmr9iM//fs29i+/zDOP3kAvnbxyTh9UMSTxx82fbFZGRKsn3u5J9uoZAzgiIiIyFtOBsgHdx0pz779jfR6qy0vmz9mj6PN13QdFU33lrKbXVEKOLz/SIVDrV/VwZ3p4hhGa7xCNcBVPwdOvChdCKMTUEphxsImNK981LDIxIrRs3H+dV8s4x5Wnht+/SJiexL497QLEAoGcm+Qx8xfW5vCVT9fhl0HDuO5r12A7qFgkfbemn4W7bhId3zw5AF4/p2d2Lw7gTOH9sHXLh6Bs0/o5+k2J859DrF4brGZ7lUB/PfrF+Loo7p7ur1KwwCOiIiomDr5WowcRgPVYDdgzEfT1e20YE1f2a57JL3Gatsq4yCqW2/g1CuPrOnaG+tY9jxQlX5t92/r2Ly3ffshoOexmVL0BiXVLXWe0uOah5atx+wn38CXLjwRHzz870wVyp14X/rhh61TsP64y/GX2yaULWCoNKs2x3HVz5dh5uWn4NbzTjC/YR7fBS+s24mP/d9LmH7pSNz+weEe77m9+oYYZixs6rCODQAGRbrj3qtH44MnD+iYLlrE7VYFBEop9OhWhZkfORXXjxtUlG1XArMAzqSeJxERETmWHczs3Zz+G/B1ep3r+x7ae6TK39+/ltvsOHUYePUP6T5hR48ETvpwpiDGqen/eh2bXv9jNktx+Y86bjuVTO+XviJjfCPw+gLj/UslgaHndehRhZ4Djvy7pj/w/84wSXHzV+paoZ5/eye+u/hNfPjUY/DVD5+MQGAEkGkbcByAya9vw+0Pv4q7FzbhvhvGdNkBshv/97930atbFaacOdj6hqff4Ppzf+7w/pg08mj8fOk7mDJ+MPr0qLa/k0cOJVO4d/EbOcEbkO6ydsGIo4u2bW2dXPb6udMH9cb0BU34+l9fw5OrtuL7V4/G4L41RduPSsMZOCIi8p9Km80yW/fS8xjgs0vTwUOVzYDMyzVdhRSZqOoOnP25dPpiezn7DcY9twwJ8O1d6VRHu22Xo8JggcUt8i55XkLrdx7EVT97Hsf1DmPBF85Fz27G5+t/8q+3cf+/3sLdl43EbeeXftankmze3YwPzluKz553AmZcdkpRtvHW+/tx8f3/RY9uQTQfThXt+GpNteH1rfuw7J2deGHdTqzYsKdDFUi9cq5Fa2tTeOSljZj79BooANMmj8AnzhmKQKDrnGzgDBwREVWGcs9muZVqNQ4mAODA+8D9p6b/3b33kZmg7MbFO98GVj6UnsEC0o/3ty+lm/MeNzZdXKOlOd1QueVg5v/N6cvfWpKbLphMAPVfAF74f9b7vv3Njs2ngfRjLftJ+t/B6iO9zaLjO65N+8vHgH2x3MfsPcg+eAPymqVod9G3jYOwi77tbLtAXsFjdrpXLJ7AjIVNAOCbIG7foSRu/cMrCAYEv/nEeNPgDQC+ctGJeOv9/Zjz9BqcdHQvXDiyeDMtle73yzYgIIJPThxatG28sXUfggHBwcP5HV9mJxeUUnjr/QN4Yd1OLHtnF156dxf2H24FAIw8thduOvt4/K0xhl0Hc1tNDIyEPXyG7gQCglvOGYoLRx6Nbz7xOu558g089do2/ODa03Hi0T3Ltl9+wBk4IiLyl0L7N5VK827g1T8Cr/zGPICr6Q9MmpkporHjSFGNA9vT/07sdr/dqu7pghvVPdL/37nW/LYnX2r9WG89bXKFAF99I72eLGBQqAHwZyuAIm/XrOCCX0qep9oUPvOHV/D82zvx8K1nY4KDghPNLa24/lcvYtOuZjzxxXNx4tG9SrCnpeHVbOne5iTOmfssJo86FvdPGev9jmaYHV99akKYd90Y1FQHEa4Ooqa6SvfvIMKhIP7WuDVnLVkoKBg9sDc27Ulg54H0yaHj+9Xg3OH9cO7w/jhneD/079kNgPFaNFf92IpMKYWFr8bwnafSqZ53XHQSbjv/BONCMp0IZ+CIiMjfUkngrX+YB0N7N6dnlKLjgYFj08FCOexYC7z0K2DVX9IzYUPPA065Alj5+9xg5pI51kFFqhVo3gXcNwIwK6b95ZVHgrXqHrmzW1YB78f+Yv1cTO87CDhqoPV9C5jJ8kQhM3h5aGtThoNrANhqcnmp/fAfa/DvtTvwvatPcxS8AUBNdRUe/Ph4XPWz53HrH1ag/osTEakp3fqrYvFytvTPL29Cc0sKt55n0ePNA2bH0Z7mJG79o/XEhiD3GySZUmjcEscVYwZi4vD+OPfEfhjUx3gdmdlaND8EbwAgIrh23CCcf/IAzFr0OuYtWYu/N23DJacdi7+8vDnvfa6ElGgjDOCIiPyu0taDubXzHaDhj0Djn9OzUhIAlMF6DAkCz8xM/ztQla5mOOjM9H/R8UC/4UcKZHj9erW1Ae/8C3jpl8C659LVFk+/Hjj7duDY0enbDKx1v91gFdDrmPTtzQKpfjZrkwpJJyzkvgDqUxMx7/BPsfVQAgO7hzEtNQJ1ju5ZWXYeOIy7Hltlen3/Xt1KuDfGFqzcgl//913cMuF43HT28fZ30IlGwvj1LePw0QeX40t/bsBDnzoTVRU+s/GDf6zJKcqRSKYwb8laVwP0ltY2PPTCekw8sR9GDSxue4mBkbDhSYKje3XDbz4xHs0tKSRaUmhuSaG5pRWJZObfh1vx0+eM+/kpBfzko7WOtl9XG/V98DKgVzf84qZx+Mfr2/C1x1fhvmfear8un5RTv6dEm2EKJRGRn5U7Ta1YWpqBNxelUxA3LksHZyMuBc74ONC8B1g81fg5n3ABsGUFsOUVILYCiL16pBx9uA/Qa2A6pbCtNfe+Dl6vVxb9OlNqfQe2ywDExn4Z444LAy//Gtj1Tjql8MxbgfGfSq9d80qh73MZGi37PeXKKy+8sxNT5zcinkjiyjEDsfi1rUgkj5xgEADVVYI/f/YcjDu+T1n28dVNe/DRXy/HuOP74I+fOSvvtLLHVmzG1//6Gj557lDMvnKUx3tZPC2tbXhz2z40bo6jYdMeNG6OY8OuZtPb/+W2CagdEkG3Kvu1mgtWbsFdj6/CQ586s6jVGIHCPlN+T+8thnPmPItte3PbhVQFBEP62Ves3LSrGa1tuXGQn14z9oEjIqpE5VwPVoygYNuqdND22uPA4b1An2HpoG3sx9Il5t1uuy2VTmnUArrGP3cM3jQSTKdd9hiQ+1+mzHzj809jxGs/QFjX7Fip9KQeouOAsz8PnHqVfTXJfFXYTGtnHzC2ptrw02ffxv9b+g6G9e+Bn914Bk4deFROytWt5w3DH17YgO37D+M3nxiPc4d7GNg7sG1vAlf+bFl6HdQXJxZcfv67T72B3z6/HnOvGY2PnjXEo73Mj1F621VjByIWT6BhU7w9YHt96z60ZKooHt2rG2qHRPDiul3Yd8jguyCjeyiAM4f2xbnD+7fPrgV11Q3T216DWPwQqgKCededjqvPKH6riXxT+rrKCRW9YdMXGyaeA8BHTj/O9v5PvbbN8PJyVt7MxgCOiKjSvL8a+OW55tdPbQIiRRpgeV2aXmuwvHdzOv3w1KvSgdvQD2QiJG+o2RGIwU+6AiDDJ6HtwA6oA9sRaN4FUeaDO72diKDfrA3sk5XFbPDkp8FPvrbtTeCORxvx8obduG7cIHznqlGoqTZfdbJ93yHc/NuXsHFXM35187iSVXM8lEzh+l+9iHd3HMATX5yIk48pvABJa6oNn/7DCry4biceuXUCzhrW14M9dc8oIAkIUFMdxIFMlcZuVQGMjvZG7ZAIxg7ug9ohERzXuztExDSg+dYVp6B/j254Yd0uvLBuJ956Pz2Df1T3Kkw4oR8mntgfiZZW/OTZtzvMtFZCMFSp67nyVehJpEo4CcUAjoh8o6v9yLjSlkoX8lj+S2DD/2C8NF3nmNHp1MMRlwDH1eZUDMz7tf7xKcC+rbmXd+sFjPuU9X1X/h44vD/38kAImPz99NqxcHFSzd6bfSKOxY6cy2OqPy5WP8fBlvRgTtCGo9CM/rIX/bAP0dB+/FgeMIwl25RgfNXjGDs4gtrBEYwdEsGYwREc1T3U4XZd6bhubmnFuO/+y7Dx78De3fHCjIvKsFfeePbN9/G1x1fhcGsbvnf1abi61tmsy+6DLfj4717C2vf246cfrcWlo+1nAAqhlMJX/tKIp17biv+7ZTw+dOoxnj323kQSV/98GfYmkvjblyaaFr4oprO+9y9s33845/JwKIi7LxuJsYP7YORxvSzTRZ18JrfvP4QX1+3CC+/swrJ1O7Flj3lBGj8N7KnwWcdKmLVkAEdEvlDOL0xfD7AP7QUaHgZefjDdNPmoQcBZt6Jhu8LIVXM6pPUlVDXeGXUHRg/qDax9Gtj0YrroR89joU6+BK0nXYKDA8/FE6/vxuv/+A3ulL9goOzEVtUfD+Cj+MDVXzjyvA8fAHasAba/Abz/Rvr/299IFxMxU2VT/bHVbAAkwOy4m1fFtTvunoE5od+gRvd6NatqTE/eiv7n3IJ+PavRv2c1+vfshn49u6Ffj2r061mNmuoq0+BvK/rj/lEL0bA5jne2p8/WiwAnDuiZDuqG9MGe5hb8v+fexqEynbEv5bG9fudB3P6nlVj7/n5UBSRnDcmIY3pi4RcmoodF/zE/amltww/+sQa/fX49Tj3uKPzsY7U4YYC7XlP7DiXxqd+/goZNe/Cj68fgmiKm3P186TuYt2Qtvn7JCHzhghM9f/x1Ow6g7ufLEI2EseDz55bk/Uy0pPD069vwl1c24+X1xi02ij3Du2lXM86ft7Qs2yb3Cv3u8/W4AAzgiMgnypWy4NszbbvWAS/9Gmh8JF2MY/AEYMLtwMgrgGAVJs59DuP2/RNfr3oMA2UXtqp++GHrDVgSOB+jo73R3JJC1eHdqD38Cs5tfQUT0YiecgjNqhveVgNximxGtRxJFWxRVVgeqMX5J/VPB2rxjUf2JVQDDBgJHHMq8OZTwKF47v46WXtXxnV758x5Fmfu/1fO67XyqA/bHl+vLPo1Tls5MydYfn3cvTjzys8BSM9MvLYljsZNcTRsTq/B2W3Q/FZTijP2pTy2n1n9Hu56bBWCQcFPP1qL3QdbOgx+zju5Px5fsQWnDTwKv/vkmejXs/zVGZ3YuOsgvvxoA17bshefOOd4zLjsFHQPOWhGbuDg4VZ89o8r8OK7u3Bv3WmuK0Ka0Q80+/Soxu6DLbhq7EA8MGVs0dJ7//PWDnzq9y/jtIFHYefBFmyLHyrKIPf12F7Mf2Uz6htj2H+oFUP71WD3wRbDNWyl+ExVQmoddQ0F9YETkUsA/ARAEMBvlFJzTW53JoDlAKYopf7q5r5E1DWY9bkpdh+leUvWelJSOm/ZBSpOuxbY/ibw9jPpkvinXZsO3AZ2LPe8NZ5ADB/AopYPdHy8tjZUVwUQqQkhXD0Eh0LD8GL1jXi9KoWTEqtw4p7/YdTmx1ElHcvxV0srzmt7BdhzSrowR+0t6YDt6FOAyNAjKZjDPli20vSFOL5vDRbt7fh6hUNBzJk8wva+Z175ObwCZKpQ7sR26Y/N46a1B28A0DscwnknDcB5Jw0AkE5j27S7GR+c92/Dx4zFE/jps29j4on9cPqgiKdNZw+3pvDG1n2Ytej1oh/bqTaFH/9zLX6+dB1GR3vjFzedgcF902l12dv40Mhj8MU/v4rrf/0i/vjps8qSfmdHHwxFakJoPtyKbqEgfnXzOFxy2rH2D2ChR7cq/O6TZ+ILj7yKbz7xOhItKdx63gkF768+SN99sAUiwAdO7F/UtZkfPHkArhwzEPWNR9Kp8ynVbjTDse9QEn9r3Ir5r2zC67F96FYVwGWjj8OUMwfj7GF9DRtTh0NBTHPwWS7UtMkjyrZtIidsZ+BEJAjgLQAfBrAFwCsAblRKvWFwu38COATgd0qpvzq9bzbOwBF1XuU6s1lwwYVCKzJmBzQAUN0LOOcLwPhPd6zAqDP2nmcQTyRzLnfyerXNjiBg8KzbFPDHixvx8XOGIhCwGPyVoTR9IZau3Y5P/f4VXDTyaKx5b39JU2LMjutQMJ1iqBTQozqIs4b1xcQT++Pc4f0x8theHV5/q1QepRS27Eng1UyJ9IZNcbyxdR9aUgb98jK8SvfafbAFX3m0Ac+/sxMfPXMwZl85ynZ26pUNu/Hph15Bj+oq/PEzZ3lSXMMrRjOWIsC3Lj8Fn/5AYYGWXktrG6bOb8Dfm97DVz98Mr486cS8g61yzghNnPssYvHcUu39elTj4VvPRr+e1ehbU23YN87ota6uCuD06FF4fes+HEq24ZTjjsKNZw3GVWOi6F3jn3Wlfk+to64h7xRKETkHwGyl1OTM3zMAQCk1J+t2UwEkAZwJ4KlMAOfovtkYwBF1Xn9+eSPuXtgxja5bVQA/uPb0ov44mg1CHA2ArCoyjr4eaDmYXjOW/d+BzP/XLAZSuYvx0XsQcOdq083+6433cesfVyAg6aBL4zQ9rvkHI1GTyC2T/L4MwNmJn2D88X3wg+tOx3CX63z8aG9zEhc/8B/0Dofw5Jc/4Ki/k5es0hg/ePIALH83XSDhhXW78O6OgwCAvj2qcc4J/XDuif3QfDiFH/9zbYeqd9XBAD50ytFoSbWhYVMcuzKpmt1DAZw+KF1QpXZIBLMXvYH39uUe2wAwaeTR+OKFJ+bdn2zV5ji+8Mir2HHgML5z5ShXZeXXvLcPH//tyzjc2obffXI8xh1fnmqG2Qr6LnCpNdWGry94DQtfjeH2Dw7HNy4Z4TiI23coiZfe3Y1l7+zEQy9sMLxNKdZkWZVqb98PAfrUVLevKe3Xsxv696jGwoZ0SmTO7QHcePYQfPTMwRgd7c0Kr0QmCkmhjALQL2bYAuDsrAePArgawCSkAzjH99U9xm0AbgOAIUPK23eEiIqnR6YUd/+e1dh1IJ0GNDDSHVeNHejsAfKc2fnwqcfgoRc25lx+xRibSnFKAf/8du7sWTIBPHE7sOgr5kU7uvVON3s2Ct4AYG/MdLMNm/bgS4++itHR3rj57CH46XPvuD4TXHPpd9D6ty+jKnVksNoa7I6jr/o+7kuOwXeeegOX/uR/uOOik3Db+Sd4mt5XarOfXI1dB1rwm4+fWfLgDTiSSmZ2xv7S0ce1VyXctjeBF97Z1V7GfHGTcS+illQb/v76exg+oAcuGHE0aoekA7YRx/TqMNtxKNmWEzx2DwUwacTRePHdXbj2ly9gwgl98cULT3SVcvfoy5sw62+rMaBXN/z19nNw+qCIq9dk5LFHYcHnz8XHf/cybvrNS/jFTWdg0kjvKiW6pZTCv9/aYRi8AcVJ464KBvCj68YgHAriV/9Zh9WxONbtPGi4luxQMoUVG/bghXU7sWzdLjRtiaNNpd/LblUBHG7NnW0dGLEpKOSBgZGw4exf/57V+O5Vp2HnwRbsOnAYOw8cxq4DLdh1oAVvbtuHXQdaDIM3zfevHl3M3Sbq1JwEcEbf9NknYx4A8A2lVCrrh8HJfdMXKvUggAeB9Aycg/0iogr0zBvv46bwS7i3x0JI6xYc7H4sZuy+Gv94faR92e3smbC9m9N/A5ZBXKIlhX++sR2f6PESbk/9GceoHXhfBuBXwY/hzy9V4Ybxg9PV5pp3Z1VjfDP93+G9xg+sUsCZn8k0gz460xi6/5EG0VWZAg6mRT2MK9Rt2HkQn/nDChzdqzt+98kzMaBXN0zJp6Hu6Tekv+R1AW9VJuC9FsB5J/fHrL+txrwla/H3pm344XWnY9TA3u63Y6CU6Uf/eP09PNEQwx0XnZSuzFkmdbVRR8/xuN5hXDtuEK4dNwhKKazfeRCT7vuP4W0FwLN3XWC7XcA4eGxuacWjL2/Gg/9dh1t++zJOH9QbX7jgRFx86jGm6bOHkil8+2+v47EVW3DeSf3xk4/Wom+ezaEH963B47efg0/9/hV89o8r8cNrT8e144rfDDnbS+/uwo+eWYtXNuxBMCBIteUOM4oVDAUCgnvrTsPWeDOWrt3ZfnksnsA3FryGZ1a/hz3NSazctActrW2oCgjGDo7gS5NOwrnD+6F2SARPN73nu/VgMy8/1fY7+9w5z2Lr3tyAuRSBJ1Fn5kkKpYisx5FgrT+AZqRn0963u68RplASdU6HW1OY+Z1v43vB/0O1OjIrdQjV+HXVLfj8Zz6D6rZDQEtzOi0xeTD972Tm7+fvBw7vy33g7r2BSd8CqnukKym2/78GCPXAg8vfw7oX6/H98CMI6majVCCE59tOQziocEZ4GwIH3u/4mEePShf4eH0BkNiTu12nVRVdNMXeeeAwrv3lC9iXSGLB5891XcY8H083bcO3/rYa8eYW3P7B4fjSpBPzrsAHlLYq4q4Dh3Hx/f/Fsb27o/6LEyt2FrHYa5wOt6aw8NUYfvnvddi0uxknHd0TX7hwOK44fSCeem1be/B39FHdEAoItsQP4cuTTsTUD52MoNU6SYcOHG7F7X9aieff2Ym7LxuJ284fXvBjOvHaljjmLVmL/729E0f36oYvX3QSwlUBfOtvq0tekdYsdRMATj3uKEw8sR/OPbE/zhzaFz0NSvZX4now31b/JaoQhayBq0K6EMlFAGJIFyL5mFLKcOGGiDyEI2vgXN1XwwCOqBNSCstXvIQRT12DPnKw3HvTTgF4vW0Y4r1OxMRzz0fgmFPTgVuvY9He1dlFAGbKQepnc0srbnxwOda8tx+P3jYBZwwpTrNrI/HmFnz3qTex4NUtGD6gB3543Rhs3t3seNCmlMJ7+w5h7Xv78ZVHG0pS/lsphS888iqefXM7nvzyBzDiWP8UynCrVAPd1lQbFjdtwy+WrsPa9/ejb48Q9h9qRTLVcSxw6weGYeZHTvVsu0A6iLzrsVV46rVt+Nz5J2D6pSOLtvbprff3475n1mLJ6vfRpyaEz18wHB8/Z2j7iYlyBEMFF1KqUCwGQpS/gvrAichlSKdJBpGuMPk9EbkdAJRSv8q67UPIBHBm97XbHgM4qjT8gTKxNwas/w/w7n+A9f8F9m81vakC8HXcgW9fcxZ69ToKCPXIzKDpZtR+fpZxKuJRUeC2f2dm7TKzdZl//99zr2PzeztxT+BBw5xuQPDoZa9hxsIm60FrkasqtqbacNufVuLfa7fjVzePw8WjCitlnq//vLUDdy9sQiyeyEk10wKKC0YMwNr39uOt9/djTeb/a9/bbxi06Xk9UP1bYwx3/KWxaI2MS62U3yNtbQrPrtmOLzyyMid4A4pX3bCtTeGeJ1fjDy9uxJnH90Fsb8LT3mIbdx3EA/96G/WNMfSorsJnzzsBn/7AUPTqHrK/c5GxtxgRucVG3kRF0iVTRMyCmebd6UBNC9p2r0vfvqYf1NDzMWfN0fiC/BWR1K6ch0z2jGLk7h/hlgnHY/aVo8y362Im7N9rt+OTv38F37hkJD7fcJVlc+lZf3sdf3hxI+ZddzquHz84n1clb0op3P1EEx59eTO+W3cabpngTePffB043Iqzv/cvHGxJ5VyXXRGzV/cqjDy2F0Yc2wsjjumFk4/phanzG7HNYN1L73AIq2Zd7Mk+bt93CB++/784YUAPPP65cwxLmJO9cswKaTOnT7/+XofL3X5v6gPeY47qhmH9e+CVDXtQFRR84tyhuP384eiT59q9YuiSvxVEVJCCGnkTkTmzBtE/XLKmc/4oGxUSqf888K97gH0xAAqo7gkcPzFd4GPY+cDRo9CwZS8efPUFfPjcE3Bm0+ycICx08Wzc+O5g/Gn5Rtw84XiceLTB2i8tSHMwE9bS2obvPPkGhvXvgU9/YCjQ17q59Lc+cire2XEA33zidZwwoEdJS57/7Ll38OjLm/GFC4aXPXgDgJ7dqtBsELwB6eBtxqUj0wHbsb1w7FHdc9LgvnHJyJyBakCAvYkk7v/nW5j6oZMKSp1TSmH6wiYcSqbwo+vHMHgrgFmFwWIWmRARvLYltzBQIpnCt+pfRyyeQE11EDXVQYSrq1ATSv+7pltV+rJQEP95azvuXfwmDmXaLry37zDe23cYHzixH358w1gcfVT3ou1/vuwqlRIROcUAjqhAZqWnt8YP4bKf/A8jj+2Fk3UzFMf17jjgrZj0S6WAXeuAp7+RW1K/rTXd7+zCbwInfBAYWAsEO6Ys/fON9xEMCE7+0GeAoX0Ng7CpJxxGfcNWzH36TfzmE2fC0Ok3OEpd/N2y9Xh350E89KlMWXmb4K8qGMDPP3YGrvr5MnzuT69i0ZcmlqRS2uMrNuO+f76Fa2qjJako55TZwD4aCeNzH7QuQGE0UL3rwyfhhXd34yfPvo1dBw/jnitPy7s4xuMrt+C5Ndvx7Y+c2il62JWTWYXBYh+LZt+b+w+3Yt6StXk/7vqdzb4M3jROK5USEVlhAEdUoP49u2HHgdw+Xz27VWFAr254Yd0uLGw40u+rV/cqjDgmHdAdSqbw5Gvb0JLp7xOLJzBjYRMAFP9H3m5NV2IPEFsJbFkBbHkl/W+jSoyaVAvwwWmmVz+z+j1MOKEveteETIOw/j274YsXnogf/GMNlr2zE/+/vfuOr7K8/z/++mSRhBUIO6zIHoYV0YpatzgQ6oRa26/VUutsHRVbq621lRb7reKopdav/urEgaOigDioxQHIDoQlSBYkgQwgO9fvj3MCIZyQk+ScnIz38/HII+e+7vu6z+fQ2z7OJ9d1fa5Jg7s16KPtKSjm8aVbOXdET84c1uPIiTqSv7jYKJ75YTLfe2o5M/+1ktd+eioxUcHbU+zTLdnc++Z6ThvcjdmXJzWrzWwb+8Xe1xfV743vS3yHKP7+6Q72Hyzjf68eU+8929Lzivj9uymcnNiV/zl1YL36yrFCNSpU+x8Ioll655kUlVZwqKyCotJyDpVWcLCkgqIyz+tDpRX88vV1Pu8bjL3cRESaGyVwIo3g2bPn2PaYyHAemjb68Jeg/ENlpO4p9PxkFbAl6wDvrs3wWfShqKyCOYtSg/sFytc0yHdugR0fe0ba0lZC7lbvxQY9RsCIKdD3JPjoD3Ag69h71rKnGcD27ANszz7ID78zsM7Qrps0kBe+2MVD723i37ee1qBRmocXbqKs0nF/A6roDenZkbkzxnL98yu56/W1PDFjXFASqw3p+fzshVUM6dmRv/1gPFG+HqQQCsYXezPj3gtH0K19O/6wcBN5RaX8/dpknyXTfXHOcc/r66hwjkeuHFPrPmZSP6EYFar9DwTDiY4MJzoynOPVYH3sw61NPvVTRKS5UAIn0gjzlm0ns6CEG05P5P31WbV+0e0cG8nExK5MTDyyrso5xwn3LvRZQMDvvyLXpzKic54RtP3fwAezjp0GWV4Ca17ybEDd9yQYM93zu884iO505LqI6OOuJfNlSYpnf7VzR/as8yNFR4Yz68Lh3Pryat5YlcZVJ9WvoMhX3+zjrTUZ3Hr2YPrHx9arb5Wzh/fknsnDmf3+Zob37Mit5wxp0H1qs3vfIf7n/1bQJTaK5647qVlUyPMlWF/sf3LGCXRtH8Uv31jH9Hmf89x1E+nWoV2d/V748ls+25bDH743mn5dG/a/rTQPjf0DQaimfoqINAdK4EQaaNveA8xduo1Lknpz38Ujue/i+o32mFnjCgj4HEW7DQoyPCNm+3fC/l2Qt+vI69LCuqKCu7Ye2f/Ml3oUEqmyeGMWoxM6keDnX8cvSerNs//9hkcWp3JxUm/a+zlCU1HpeOCdjfTpHN3osvI/PeMEtmQV8pclWxjSsyOTRzeurH/1tY7hYUZEGLx86+n0bMbrdYLp8gl96do+ip+9uIor/racf11/8nGTsl25B/nje5s4fUg3vj+xfxNGKsHSmD8QqCCIiLRlSuBEGqCy0vGrN9cTExXOA1NqKXnvB19/RTbg9nPqSD4qK2DxfT5G0YrgwweOHEfEQJcBEDfAUxWy6vV7d8CBPcfet3Pf4ydvVfwsJAKecu+rd+fxi3OH+nU9eJLb+y4eyeV/W87fl+3gjvP86/vSl7vYlFnAk98f3+i1a2bGHy87ke05B7lj/hoGxJ/KiN6d6u7oQ83y4eWVjrCwMDZmFDCkZ8vdfLqxzhregxdvOIUfP7eCy/62nP/344k+/40rKx13v7aOiHDjz1c0r7WCEjoqCCIibZUSOJEGeHnFt3y1cx9/viKJ7h3rnvpVm2njEkjY/W/6fT2HHi6bLOvG7NKr+GJHAlcm9zvyRfXA3iPFRNJWQMZqKD1Q+42vX+JJ1Dr08J2QlR2q9zTIhvpw016cg/P8mD5Z3YQBXbgkqTfzlm1nxsR+9O58/NG7fQdLeWTxFk4dFM9FJwZmE+zoyHD+ce0EpjzxGTPmfUF0VDh78uu36XBFpePh9zcds9VEaXll8Nc6tgATBnThtRu/ww//+RVX/f1znvlhMiefEH/UNc/+9xu+2rmPR64cU+dzICIi0topgROppz0FxcxeuJlTB8Vz5YTaC3f4Zd18Tlr/AFAEBn3I4S/R/+TxdVl87QYzIWK7J2HL+9ZzfVgE9BwNY2bAhjegaN+x9+zcD/pNPP77NmAaZEMtScmiX9cYhveq/0jTPZOHszhlD3MWpfK/V4097rWPLE7lQEk5v710VEBHaHp0iuaak/vzv0u2QlEZcHS10Klj+7DvYCm79xexe98hvt13iLT9h9i9r4hv9x0iI6+I8kpfKx1VMa/K0J4deeOmU7n2n19y7bNf8cSMcZw/ypOEb9t7gD8vSuXcET25fHzbTnZFRERACZxIvd3/9gZKKyp5+LITG58oLH3wmGmQkZUl3BH5JmyG0g4JRPU/CSb+FPomQ+8xnpEy8CRpjRlFq8c0yIY6UFLOf7flcu13BjTo36pf11h+PCmRpz/dzv+cOpCkvnE+r9uQns/LX33LdacmMjQIUxJfXZF2TFtRWQV3vbaWXy9Yz8Eam17Ht4+ib9dYxvSL45Kk3rz05bfkeZO/6lQx74iEuBhev/FUrntuBTe+sIqrkvuxbGs2GXnFmMEZQ+M1dVJERAQlcCL18sGGTBZt3MO9Fw5nQHz7xt3MOc/ol69TwKVRz7C/Ip73Ljnds3daTU04itZQn6ZmU1pRyfn1nD5Z3U1nDeK1lbt56L1NvDrzlGO+xFdWOu5/ewPx7aP4+XmBrRZZpbaRsvJKxw+S+9G/ayz9usbSr2sM/brEHlN0ZWjPjqqY54eu7aN46YaTuexvy3llxe7D7c7BwwtT6RQd1eannIqIiDSvjYdEmrH8ojJ+8/ZGRvXpxPWnJTbuZt8sg3+eDz43EQDr3I8Hf3AuewqKuWP+GiprmYJH0lXwiw3w2zzP72aUvIFn+mSX2EgmDDjejk7H1yk6kp+fN5SvvtnH4pRjC68sWJ3O19/m8cvJw+kUpHL8tY2UJcTF8NtLR/Hj0xI5b2RPhvfq5LNi5rRxCTx82YkkxMVg3n4PX3aikhEf2reLoNDHaGXV/ogiIiJtnRI4ET/Nfn8T+w6W8qfLk4gIb+B/Omkr4flL4fkpUJAO4649MiWyinca5Lj+Xbjv4pEs3byXp5dtb/wHaGJlFZUs3byXc0b0bPi/l9eMk/oxuEcHHl64idLyysPthcVlPPz+Zsb2i+OK8Y1cj3gcd18wjJjIo6ta1ncEbdq4BP4762y+mX0x/511tpK348jML/bZrjWDIiIiSuBE/PLFjlxe/mo3N5yWyOiEzvW/QdYGeHkGPHMO7NkIFzwMt34NU5+AKXM9hUcwz+8pcw+PpP3wOwOYMqYPjyxKZfn2nIB+prdWpzNp9kckznqPSbM/4q3V6QG9/5c79lFYXN6o6ZNVIsLD+PXFI9iZe4h/fbHrcPvcpVvJPVjCg1NHERYWvPVRGkFrWrWNeGrNoIiIiNbAidSpuKyCe99cT/+usfy8HnuZAZC7HT7+o6diZLtOcPZv4OQboV2HI9ccp5iImfHwZSeSkpHPbS+v5r3bArPxc819yapXVQxUUrIkJYvoyDBOH9I9IPc7c2h3Th/SjblLt3L5+ARyDpTwf//dydXJ/WotbhJI2nOq6fjaH1FrBkVERDyUwIkArJtfazGQxz/ayjc5B3nxhpNr3xy6Zv/v3OwZaVvzEkS0g9PvgFNvhZj6rwXr0C6Cv/1gAlOf+C+3vrSaF39yMpGNnJI4Z1HqMfuSVa0xCkSS4pxjccoeTh/SvdEbalcxM3598QgmP/ofJv3pIw6WVGDA6ISGba4tzVfVMzhnUSoZeUX12ndPRESktVMCJ7Ju/tHl+PN3e46BlG6T+funO7hiQl8mDe7mf/8PZoGFw8SZnuStQ49GhTi0Z0dmX34it7+yhkcWpXLvRSMadb/a1hIFao3RhvQCMvOLueO8eo5Y1mFzZiHhYcbBEk/y6YA/vLeZDu0i9eW+ldGIp4iIiG9K4ER87MVGWRHuvbtYEf4Zt0eXc318Ivxnqe/+/3302P7gSdounB2wMKeOTWDFzn38fdkOxg/owgXejY795ZzjP1tzePLjbbXUvgzcGqMlKVmEGZwzovHr36qbsyiVihoVOQM5cigiIiLS3CmBk7atsrLWvdisJJ8f8bznYFkD7l2Y1fC4avGbS0ayLi2fu+avZfhtHf3ai66y0jOd8alPtrEuLZ9enaKZNrYPH2zMorjsSEXH8DAL2BqjxSl7SB7Yla7towJyvyrBHjkUERERae6UwEnbVFIIq1+Er/5ObXuxZbh4fjfgXzx97XiM41Q4fHwCFPhIAjsHvqx9u4hwnvz+eC55/DNufOFrFtx0KtGRvteYlVVU8u7aDJ76ZDvb9h5gQHwssy87ke+NT6BdRDhvrU4/vMYotl04B0sqAlIg5dvcQ2zOKuS+ixs3zdOXPnExpPtI1lSdUERERNoKbSMgbcu+HfD+LPjLCPjgHoiNh5NuOGYvthJrx1/dDB64bDwWGQOR0bX/nPtArXu5BUO/rrE8evVYNmUW8MDbG485X1xWwQtf7OKsRz7hjvlriQgz5s4Yx9I7vsv0if1pF+FJ+KrvS7by1+cxID6We95Yx6HS8kbFtzjFM/J4/sj6TfH0RyD2YxMRERFpyTQCJ62fc/DNMvjyaUh9H8LCYdT34OSfQd8Jnmv6nXy4iuShmF7Myv8eEy75iX8jO1VbANRSxTIYzhreg1vOGswTH29jUUoW+YfK6NU5mpMGduHzHfvILixhbL84fjtlFGcP71HnHmkxUeH86fIkps/7gkcWbeH+KSMbHNvilD0M79WR/vGxDb5HbVSdUERERNo6JXDSetQs5X/mLKisgC//Dns3ekbbzrgLkq+HTr2P6vpWxSTmlMwlo7gIK4F+XWL46ykD/H/v4+zlFiwndGtPmEHeoTIAMvOLeWdtJkN6tOexq8fynUHxmPm/ufUpJ8Rz7SkD+L/l33BxUi8mDOha75j2HSxl5c593HLW4Hr39ZeqE4qIiEhbpimU0jpUlfLP3w04z++3b/a0WRhMfRJ+kQJn33ds8ubd1Do9rwgHVDrIKijh3bUZIfko/vrLki1U+li+d6i0klMHd6tX8lblnguH06dzDHe/vo7iGvvE+WPppj1UOji/nhUyRURERMQ/fiVwZjbZzFLNbJuZzfJxfqqZrTOzNWa20sxOq3Zup5mtrzoXyOBFDvO1FQBA++5w439g3A8869V88LWpdUl5JXMWpQYj0oAJRkXGDu0imH35iezIPsijH26td//FKXvo0zmaUX20ubaIiIhIMNSZwJlZOPAkcCEwEphhZjUXyCwFxjjnxgI/Bp6pcf4s59xY51xy40MWqaGy0jvy5sPBHKhjJKqllqavbX1eYysynj6kO9NP6se8ZdtZuzvP735FpRX8Z2s2543s2aDRPxERERGpmz8jcBOBbc65Hc65UuAVYGr1C5xzB5xzVZO52lNbXXaRQMtcB89eUPv545TyLy2v5NEPtwR9U+tgCWZFxl9dPIIeHaO5+/W1lJT7N5Vy2dZsissqNX1SREREJIj8SeASgOrDG2netqOY2ffMbDPwHp5RuCoOWGxmq8xsZm1vYmYzvdMvV2ZnZ/sXvTRLb61OZ9Lsj0ic9R6TZn/EW6vTA/8mxQWe7QDmfdezNcCE6+pVyn/t7jwufeIzHv1wK+P7xxEdefR/Ci2hNP20cQk8fNmJJMTFYEBCXAwPX3ZiQAp8dIqO5I+XjWbLngM8+dE2v/osSdlDp+gIJibWv/iJiIiIiPjHnyqUvuZCHTNo4ZxbACwwszOA3wPnek9Ncs5lmFkPYImZbXbOLfPRfx4wDyA5OVkjeC1UVUGQqjVl6XlF3PvmeoDAVA50Dja8AYt+BQf2QvKP4ZzfQEwXGHBqnaX8i0orePTDLfzjPzvo0TGaZ36YzLkjex61qXVLKk0fzIqMZw/vyWXjEnjqk+1cMLoXo/p0rvXa8opKlm7aw9nDexAZrtpIIiIiIsHiTwKXBvSrdtwXqLU8n3NumZkNMrNuzrkc51yGt32vmS3AMyXzmAROWgdfBUGKyiqYsyi18YlG9hZYeKdnT7feY2HGy5Aw4cj5Okr5f7Ejl1lvrGNn7iFmTOzHvReNoFN0JKDS9LW5f8pIlm3N4e7X1vH2LZNqTc5W7trP/kNlmj4pIiIiEmT+/Kl8BTDEzBLNLAqYDrxT/QIzG2zeqgVmNh6IAnLNrL2ZdfS2twfOBzYE8gNI8xKUgiClh+DD38HfToXMtXDxX+AnHx2dvB1HYXEZv16wnunzvqDSwUs3nMzDlyUdTt6kdnGxUTw0bTQpmQX8/dPttV63JGUPURFhnDG0exNGJyIiItL21DkC55wrN7NbgEVAOPCsc26jmd3oPf80cDnwQzMrA4qAq51zzsx64plWWfVeLznnPgjSZ5FmoEenduwpKDmmvXdn3yX8j1FzM+4RU2DTvyH/WxjzfTjvQejgf5Lwcepefv3mejILirn+tETuPH8osVHav74+Jo/uxSVJvZm7dBvnj+rF0J4djzrvnGNxShaTBsXToZ3+bUVERESCya9vW865hcDCGm1PV3v9J+BPPvrtAMY0MkZpIZxzxMVG+UzgwsOMtP2H6NsltvYbVG3GXbWfW/5u+OIp6NgH/mchDJx03Pevvo6tV+doEuKiWbkrjyE9OvDGz05lfP8ujfl4bdrvLh3F8u253P3aWt742alEVJtKuTmrkN37irjpzMEhjFBERESkbVC1AQmY11alkZpVyOXjE46qjPjjSQPJO1TGlMc/4z9bj1NhdOnvfG/GHRbmV/J275vrSc8rwgGZ+cWs3JXHBSN78O/bTlPy1kjxHdrxu0tHsTYtn39+9s1R55ak7MEMzhnRI0TRiYiIiLQdmu8kAbGnoJjf/zuFiYldmXPFGMLCji5e+oNTBnDjC6v40bNfcef5w/jZdwd5rik9BDs+gdSFnmmTvuTXvQ3BnEWbjymeArAho5B2EeE+ekh9XZLUm3fXZvCXJVs4d2RPBnXvAMDilCzG9YujR0c/p8mKiIiISINpBE4azTnHrxesp6yikj9fnnRM8gZwQvcOvHXzJC5J6sNzi77gX08+SNm/roQ/J8IrMyDl7WP3catynM24nXMs3bSH9Lxin+cbVTxFjmJmPDRtNDGR4dzz+joqKh3peUVsSC9Q9UkRERGRJqIROGm0d9Zm8OGmvdx38QgGdmt/bCGSc+6HHiOJ3fI+jx14H4teBbmQta87UaNm0HXcVBgwCVLeOnoNHNS6GXdFpWPh+kye/Hgbm7MKCTejwh27fWCfuFqSQmmQHp2iuf+Skdz52lrG/34x+UXlAPjI2UVEREQkCJTASaNkF5bwwDsbGdc/jusmJfouRPLmTKr2freECXD2fWzoOInr3jvIgbUVzB48lKkRUUf2cDvOZtyl5ZUsWJ3G05/u4Jucgwzq3p6/XDkGcNz31sajplHGRIZz9wXDmuhfou0IM89PVfIG8NclW+nRMVp76YmIiIgEmRI4aZQH3tnAodIK5lyRRHiYeZKvYwqROIjpAjd9AR09U+1GA+8NKuaWl1Zz+ytrWP1tHr+6aARRtWzGXVRawSsrvmXesh1k5hczOqETf7tmPBeM6nV4ymZ4WNjhKpR94mK4+4JhSiiC4JHFW6isMdgZsM3aRUREROS4lMBJgy1cn8nC9Vn8cvIwBvfw7g2Wv9v3xUV5h5O3Kj06RfPiT05m9vub+edn37A+PZ9Lx/Rm3rJvDidht5w9iH0Hy3j2s2/IPVjKxIFdmX15EmcM6YZ3f8HDpo1LUALRBIKyWbuIiIiI+EUJnDTIvoOl3P/2Bk5M6MzM008A5+DzJ2rvUEshksjwMH5zyUjG9ovjjvlrWLVr/+Fz6XlF3PvmBgDOHNadm84czMTErgH9HFJ/feJiSPeRrGm9oYiIiEjwqQqlNMjv3t1IflEZc65MIoJKWHgXLL4P+oyHiBpf5GspRFLdlDF96BIb5fNc947teO66iUremom7LxhGTOTRWzNovaGIiIhI01ACJ/W2JGUPb6/J4OazBjO8Sxi8eg2seAZOvQ1uWAqXzoXO/QDz/J4y1+e6tpqyC0t8tufU0i6hMW1cAg9fduJRm7U/fNmJmr4qIiIi0gQ0hVLqJf9QGb9esJ7hvTpy04QO8NxFkLUeLnoEJv7Ec1EthUjqoql5LYfWG4qIiIiEhkbgpF4eei+F3IOlPH5ONFHPnQ8522DGK0eSt0bQ1DwRERERkePTCJz47ZPUvby2Ko0/jd/HkH//1LO27bqF0GdsQO5fNaKjrQBERERERHxTAid+KSwu41dvruemuC+5avOTED8ErnkN4voF9H00NU9EREREpHZK4MQvDy/cxPSDL3BbxJuQ+F24+l8Q3TnUYYmIiIiItClK4KROn6dmMGH1vVwe8RmMvQYueRQifJf8FxERERGR4FECJ8daNx+WPgj5aVR27E2vwjC+E55G2Rn3EnnWPWAW6ghFRERERNokJXBytHXzKX/7ViIqigEIK8xgoIM9w66h59mzQhyciIiIiEjbpm0E5CiH3r//cPJWxQw67v4oRBGJiIiIiEgVJXByROkhYooyfZ6KLspq4mBERERERKQmJXACRXmw7BF4dDS1rW7LqIxvyohERERERMQHJXBt2YFsT7GSR0+Ej34PfcbzbPiVHHJHV5g85KJ4JuoHIQpSRERERESqqIhJW5SfBssfh1XPQ3kxjJwKp98BvcfQdXU6v36jB3eGvUofyyXDxfMo0znt4pmhjlpEREREpM1TAtdaVdsKgM594Zz7IWECfPZXWPsK4CDpajjtF9BtyOFuExO7cmfFJD6wMyguraBPXAx3XzCMaeMSQvdZREREREQE8DOBM7PJwGNAOPCMc252jfNTgd8DlUA58HPn3Gf+9JUgWDcf3r0Nyoo8x/m7YcGN4CogIhqSr4NTb4W4/sd0ffTDLYSHhbHkjjPo2yW2iQMXEREREZHjqTOBM7Nw4EngPCANWGFm7zjnUqpdthR4xznnzCwJmA8M97OvBNrSB48kb1VcBbTrCLd+DR16+Oy2dU8hr69K47pJiUreRERERESaIX+KmEwEtjnndjjnSoFXgKnVL3DOHXDOOe9he8D521eCID/Nd3vJgVqTN4A5i1JpHxXBzWcNDlJgIiIiIiLSGP4kcAnA7mrHad62o5jZ98xsM/Ae8OP69PX2n2lmK81sZXZ2tj+xS2069fHd3rlvrV1W7drP4pQ9zDzjBLq2j6r1OhERERERCR1/EjhfW4O5YxqcW+CcGw5Mw7Mezu++3v7znHPJzrnk7t27+xGW+FRRBtFxx7ZHxngKmfjgnONP72+mW4d2XH96YnDjExERERGRBvMngUsD+lU77gtk1Haxc24ZMMjMutW3rzSSc/Dvn8PejTD+h9C5H2Ce31PmQtJVPrt9nLqXr3bu4/ZzBhMbpcKkIiIiIiLNlT/f1lcAQ8wsEUgHpgPfr36BmQ0GtnuLmIwHooBcIK+uvhJAn8yG1S/Ad2fBWff61aWi0vHnD1IZGB/L9InHVqUUEREREZHmo84EzjlXbma3AIvwbAXwrHNuo5nd6D3/NHA58EMzKwOKgKu9RU189g3SZ2nbVj0Pn86GcT+AM2f53e3tNelszirk8RnjiAz3Z0BWRERERERCxY4Uj2w+kpOT3cqVK0MdRsuxZTG8PB0GnQUzXoHwSL+6lZRXcPYjn9KlfSTv3HwaYWG+liyKiIiIiEhTM7NVzrnkmu0acmnp0lfBaz+CXifClc/7nbwBvPjFt6TnFXHP5OFK3kREREREWgAlcC3Zvh3w4lXQvjtc8xq06+B318LiMp74eBuTBsdz+hBV/RQRERERaQmUwLVUB3PghcvBVcIP3jzuBt2+/OM/37DvYCn3TB4epABFRERERCTQVDO+JSo9BC9dDQUZ8KN3odvgenXPLizhmf/s4OITe5PUNy44MYqIiIiISMApgWtpKsrhjesh42u46l/Qb2K9b/H4R1spKa/kzvOHBiFAEREREREJFiVwLYlz8P7dkLoQLnoERlxS71vsyj3IS19+y9Un9eOE7v6vmRMRERERkdBTAteMvbU6nTmLUsnIK6JPXAz/SPyEkZuehdN+ARN/0qB7/mXxFiLCjdvPGRLgaEVEREREJNiUwDVTb61O57MFT/Eqr9CnXQ55RR3ouukAu/tOod85DzTonhvS83lnbQY3nzWInp2iAxyxiIiIiIgEm6pQNlNr3pvHgzaPvmE5hBl0tQNUOOP/MgeCNWzPtj8vSiUuNpKffndQYIMVEREREZEmoQSumbqh9AVirfSotnBz/LjsZZxz9b7f8u05LNuSzc1nDqZTtP+bfYuIiIiISPOhBK6Z6hOW67vdcpn65H9ZsDqN0vJKv+7lnONPH6TSp3M0135nQCDDFBERERGRJqQErpnKi/C9MXdeZA8OlJTzi1fXMulPH/HYh1vJOVBy3Ht9sCGLtbvz+Pl5Q4mODA9GuCIiIiIi0gSUwDVDu/cd4sXS06g5UbI8PJqulz7Eh7/4Ls9ddxIje3firx9u4dSHP+Ku19ayMSP/mHuVV1QyZ1EqQ3p04PLxfZvmA4iIiIiISFCoCmUz45zjvtdX8oB9TmW7LoS3i4WCDOjcl4hz7oekqwgDzhzWgzOH9WDb3gM8v3wnr69K4/VVaUxM7MqPJw3kUEkFf1myhfS8IgCunzSQ8LCGFT8REREREZHmwRpSECPYkpOT3cqVK0MdRki89OW35L/7K34W8S5cuwAGne1Xv/xDZby68lueX76L9LwiDI4awYuJDOPhy5KYNi4hKHGLiIiIiEjgmNkq51xyzXZNoWxG0vOKeGfhu/w04j3cuGv9Tt4AOsdGMvOMQXx695l0bR95zPTLojLPVEoREREREWm5lMA1E8457n9jFQ/yNyrb98Qu+EOD7hMRHsb+g2U+z2V4p1OKiIiIiEjLpASumXh9VRpJ3/yTobabiKmPQXTnBt+rT1xMvdpFRERERKRlUALXDOwpKGb+vxdyc8TbuKSrYegFjbrf3RcMI6bGdgExkeHcfcGwRt1XRERERERCS1UoQ8w5x/1vruG37ilo3xWbPLvR96wqVDJnUSoZeUX0iYvh7guGqYCJiIiIiEgLpwQuxN5Zm8Hgrf9kVOROmPICxHYNyH2njUtQwiYiIiIi0soogQuh7MIS/t/bH/BK5JtUjvweYSOmhDokERERERFpxrQGLoQefHsd91c8iUV3IuyiOaEOR0REREREmjmNwIXI++sz6bXpWcZEboeL/wkduoc6JBERERERaeb8GoEzs8lmlmpm28xslo/z15jZOu/PcjMbU+3cTjNbb2ZrzGxlIINvqfYfLOUfby3mrsjXqRx6EYy+PNQhiYiIiIhIC1DnCJyZhQNPAucBacAKM3vHOZdS7bJvgO865/ab2YXAPODkaufPcs7lBDDuFu3Bd9bzq7IniYiOIWzKX8Es1CGJiIiIiEgL4M8I3ERgm3Nuh3OuFHgFmFr9Aufccufcfu/hF0DfwIbZenyYsofOG54jOSyV8ItmQ8deoQ5JRERERERaCH8SuARgd7XjNG9bba4H3q927IDFZrbKzGbW1snMZprZSjNbmZ2d7UdYLU9+URlPLljCrMhXqRx0LoyZEeqQRERERESkBfGniImv+X3O54VmZ+FJ4E6r1jzJOZdhZj2AJWa22Tm37JgbOjcPz9RLkpOTfd6/pfvjvzfwy5IniWwXSdilj2nqpIiIiIiI1Is/CVwa0K/acV8go+ZFZpYEPANc6JzLrWp3zmV4f+81swV4pmQek8C1Vm+tTmfOolTS84r4fvhSvhOZApMfg86aZSoiIiIiIvXjzxTKFcAQM0s0syhgOvBO9QvMrD/wJnCtc25Ltfb2Ztax6jVwPrAhUME3d2+tTufeN9eTnldEH3K4N+IllrvRvGXnhjo0ERERERFpgeocgXPOlZvZLcAiIBx41jm30cxu9J5/GrgfiAeeMs+0wHLnXDLQE1jgbYsAXnLOfRCUT9IMzVmUynkVn/LLqPkkWI5nMWD5WJYs3sK08RqBExERERGR+vFrI2/n3EJgYY22p6u9vgG4wUe/HcCYmu1tRXLBEh6OfIZYKwU8iwl/GTGf/QWdgLNDGpuIiIiIiLQ8fm3kLQ1zb9Rrh5O3KrFWyr1Rr4UoIhERERERacmUwAVRT3zvXV5bu4iIiIiIyPEogQsiq6XSZG3tIiIiIiIix6MELpjOuZ+ymssMI2PgnPtDE4+IiIiIiLRoSuCCKekqPifJu+u5Qed+MGUuJF0V4sBERERERKQl8qsKpTTM3sJiSipgf6fBdL17VajDERERERGRFk4jcEG0KbOQQZaB6zYk1KGIiIiIiEgroAQuiDan5dLf9tK+z8hQhyIiIiIiIq2AplAGUc63m4mwSiJ6DQt1KCIiIiIi0gpoBC6ISvds9rzQFEoREREREQkAJXBBUlxWQYfCHZ4DJXAiIiIiIhIASuCCJDWrkBMsg+KYntCuY6jDERERERGRVkAJXJCkZBYwyDIgfmioQxERERERkVZCCVyQbMrI5wTLpF3v4aEORUREREREWglVoQySjLRddLJDWv8mIiIiIiIBoxG4IKisdFRkb/EcKIETEREREZEAUQIXBLv3H6JP+W7PQTetgRMRERERkcBQAhcEm7wFTCoiYqFjn1CHIyIiIiIirYQSuCBIyShgUFgG1m0IhOmfWEREREREAkPZRRCkZBYwNDyLsO6aPikiIiIiIoGjBC4IdmTk0NNla/2biIiIiIgElBK4AMs7VEq7gm8Iw0H84FCHIyIiIiIirYgSuADblFnIIMvwHGgETkREREREAkgJXICleCtQOgziB4U6HBERERERaUX8SuDMbLKZpZrZNjOb5eP8NWa2zvuz3MzG+Nu3tdmUWcDIqCwsrj9ExoQ6HBERERERaUXqTODMLBx4ErgQGAnMMLORNS77Bviucy4J+D0wrx59W5WUjAKGRezR9EkREREREQk4f0bgJgLbnHM7nHOlwCvA1OoXOOeWO+f2ew+/APr627c1KS2vZPveAvpUpCmBExERERGRgPMngUsAdlc7TvO21eZ64P369jWzmWa20sxWZmdn+xFW87M9+wDxFTlEVRZDN1WgFBERERGRwPIngTMfbc7nhWZn4Ung7qlvX+fcPOdcsnMuuXv37n6E1fykZBQwKEwVKEVEREREJDgi/LgmDehX7bgvkFHzIjNLAp4BLnTO5danb2uxKbOAYeGZngMlcCIiIiIiEmD+jMCtAIaYWaKZRQHTgXeqX2Bm/YE3gWudc1vq07c1ScksYHz7bIjuDO1b5iiiiIiIiIg0X3WOwDnnys3sFmAREA4865zbaGY3es8/DdwPxANPmRlAuXc6pM++QfosIeWc84zAxWRBl6FgvmaPioiIiIiINJw/Uyhxzi0EFtZoe7ra6xuAG/zt2xplFRSz/1AZvSN3Q7fzQx2OiIiIiIi0Qn5t5C1125RZQAcOEVuSDd2GhDocERERERFphZTABUhKRgEnmLeASbwSOBERERERCTwlcAGyKbOQkzvmeA5UgVJERERERIJACVyApGQWMD42B8IioGtiqMMREREREZFWSAlcABwsKWdn7kGGhGdAl0QIjwx1SCIiIiIi0gopgQuAzVmFOAe9ytI0fVJERERERIJGCVwApGQWEE4F7Q/uUgVKEREREREJGiVwAbAps4Dh0fuxilIlcCIiIiIiEjRK4AIgJaOA73bd7znQFEoREREREQkSJXCNVFHpSM0qZGxMtqchfnBoAxIRERERkVZLCVwj7cw9SFFZBYPDMqB9d4jtGuqQRERERESklVIC10ibMgsA6Fm6W9MnRUREREQkqJTANVJKRgERYUZs4Q4VMBERERERkaBSAtdIKZkFjO9WgR3KhXglcCIiIiIiEjxK4BppU2YBp3XJ8xxoCqWIiIiIiASRErhGyD1Qwp6CEsbG7vU0aAqliIiIiIgEkRK4RtiUWQjAIDIgvB3E9Q9xRCIiIiIi0popgWuElMx8ALqX7vbs/xYWHuKIRERERESkNVMC1wibMgvp1SmaqP3bNH1SRERERESCTglcI6RkFJDUOxr271QBExERERERCTolcA1UXFbB9uwDnBKXD65CI3AiIiIiIhJ0EaEOoKXatvcA5ZWOMdE5ngYlcCIiIiIiEmQagWuglIwCABJJ9zRoE28REREREQkyJXANlJJZQGxUOHFFu6BTArTrEOqQRERERESklfMrgTOzyWaWambbzGyWj/PDzexzMysxs7tqnNtpZuvNbI2ZrQxU4KGWklnAsF4dCcvdqumTIiIiIiLSJOpM4MwsHHgSuBAYCcwws5E1LtsH3AY8UsttznLOjXXOJTcm2ObCOcemzAJG9uoIOVtVgVJERERERJqEPyNwE4FtzrkdzrlS4BVgavULnHN7nXMrgLIgxNjspO0vorC4nPHxpVBSoPVvIiIiIiLSJPxJ4BKA3dWO07xt/nLAYjNbZWYza7vIzGaa2UozW5mdnV2P2ze9TZmeAiYnRu/xNGgKpYiIiIiINAF/Ejjz0ebq8R6TnHPj8UzBvNnMzvB1kXNunnMu2TmX3L1793rcvumlZBZgBgMrvRUoNYVSRERERESagD8JXBrQr9pxXyDD3zdwzmV4f+8FFuCZktmibcosIDG+PVF52yGyPXTqE+qQRERERESkDfAngVsBDDGzRDOLAqYD7/hzczNrb2Ydq14D5wMbGhpsc5GSWcCIPp28BUyGgPkapBQREREREQmsiLoucM6Vm9ktwCIgHHjWObfRzG70nn/azHoBK4FOQKWZ/RxPxcpuwALzJDgRwEvOuQ+C8kmaSEFxGbv3FTH9pP6wZiv0PyXUIYmIiIiISBtRZwIH4JxbCCys0fZ0tddZeKZW1lQAjGlMgM3N5sxCAEZ3j4T8b6HbtSGOSERERERE2gq/NvKWI1Iy8gEY1c5bKVMVKEVEREREpIkogaunTZmFdG0fRXzxTk+DKlCKiIiIiEgTUQJXTymZBYzo3RHL2QoYdB0U6pBERERERKSNUAJXD+UVlaTuKWRk706QuxW6DIDI6FCHJSIiIiIibYQSuHrYkXOQ0vJKRvbpBDlbNH1SRERERESalBK4etiUWQDAiF4dIGebEjgREREREWlSSuDqISWjgKjwMAZF5UF5EcQPDnVIIiIiIiLShiiBq4eUzAKG9OxA5P5tngaNwImIiIiISBPyayNvAeccKRkFnD28B+Qs9zQqgRMRERERCbiysjLS0tIoLi4OdShBFx0dTd++fYmMjPTreiVwfsouLCH3YCkjeneCnK0QHQftu4U6LBERERGRVictLY2OHTsycOBAzCzU4QSNc47c3FzS0tJITEz0q4+mUPopxVvA5KgKlK34YRIRERERCZXi4mLi4+NbdfIGYGbEx8fXa6RRCZyfUg5XoPSOwGn6pIiIiIhI0LT25K1KfT+nEjg/vLU6nceXegqXXPnYB3AgC7qpAqWIiIiIiDQtJXB1eGt1Ove+uZ6isgoAYgp2APBFQXwowxIREREREa+3VqczafZHJM56j0mzP+Kt1emNul9eXh5PPfVUvftddNFF5OXlNeq966IErg5zFqUeTt4ABlkGAI+taRtDuiIiIiIizVnVgEt6XhEOSM8r4t431zcqiastgauoqPBx9RELFy4kLi6uwe/rD1WhrENGXtFRxyeEZVLmwllZ0ClEEYmIiIiItB2/e3cjKRkFtZ5f/W0epRWVR7UVlVXwy9fX8fJX3/rsM7JPJx6YMqrWe86aNYvt27czduxYIiMj6dChA71792bNmjWkpKQwbdo0du/eTXFxMbfffjszZ84EYODAgaxcuZIDBw5w4YUXctppp7F8+XISEhJ4++23iYmJacC/wNE0AleHPnFH/yMPsgx2uZ70iOsYoohERERERKRKzeStrnZ/zJ49m0GDBrFmzRrmzJnDV199xR/+8AdSUlIAePbZZ1m1ahUrV65k7ty55ObmHnOPrVu3cvPNN7Nx40bi4uJ44403GhxPdRqBq8PdFww7ag3cIMtgpyVw9wXDQhyZiIiIiEjrd7yRMoBJsz8ivcasOYCEuBhe/el3AhLDxIkTj9qnbe7cuSxYsACA3bt3s3XrVuLjj66RkZiYyNixYwGYMGECO3fuDEgsGoGrw7RxCTx82YkkxMUQQQWJYXvoP3QM08YlhDo0EREREZE27+4LhhETGX5UW0xkeEAHXNq3b3/49SeffMKHH37I559/ztq1axk3bpzPfdzatWt3+HV4eDjl5eUBiUUjcH6YNi7Bk7DlbofHyxk6cnyoQxIRERERETg8sDJnUSoZeUX0iYvh7guGNWrApWPHjhQWFvo8l5+fT5cuXYiNjWXz5s188cUXDX6fhlACVx85Wzy/tYm3iIiIiEizcXjAJUDi4+OZNGkSo0ePJiYmhp49ex4+N3nyZJ5++mmSkpIYNmwYp5xySsDe1x9K4OojZ6vntzbxFhERERFp1V566SWf7e3ateP999/3ea5qnVu3bt3YsGHD4fa77rorYHFpDVx95GyB9j0gpkuoIxERERERkTZICVx95GzV9EkREREREQkZvxI4M5tsZqlmts3MZvk4P9zMPjezEjO7qz59W5ScLdBtSKijEBERERGRNqrOBM7MwoEngQuBkcAMMxtZ47J9wG3AIw3o2zIczIWifUrgREREREQkZPwZgZsIbHPO7XDOlQKvAFOrX+Cc2+ucWwGU1bdvi6EKlCIiIiIiEmL+JHAJwO5qx2neNn/43dfMZprZSjNbmZ2d7eftm1BuVQVKjcCJiIiIiEho+JPAmY825+f9/e7rnJvnnEt2ziV3797dz9s3oZwtEBENnfuFOhIREREREalu3Xz462j4bZzn97r5Tfr2HTp0aLL38ieBSwOqZy19gQw/79+Yvs3Huvnw1T+gvBgeG9PkD4SIiIiIiNRi3Xx49zbI3w04z+93b2u139n92ch7BTDEzBKBdGA68H0/79+Yvs1D1QNRXuw5rnogAJKuCl1cIiIiIiJtwfuzIGt97efTVkBFydFtZUXw9i2w6nnffXqdCBfOrvWW99xzDwMGDOCmm24C4Le//S1mxrJly9i/fz9lZWU89NBDTJ3a9OU96hyBc86VA7cAi4BNwHzn3EYzu9HMbgQws15mlgbcAdxnZmlm1qm2vsH6MEGx9EHPA1BdWZGnXUREREREQqtm8lZXux+mT5/Oq6++evh4/vz5XHfddSxYsICvv/6ajz/+mDvvvBPn/F1ZFjj+jMDhnFsILKzR9nS111l4pkf61bdFyU+rX7uIiIiIiATOcUbKAM+at/zdx7Z37gfXvdegtxw3bhx79+4lIyOD7OxsunTpQu/evfnFL37BsmXLCAsLIz09nT179tCrV68GvUdD+ZXAtWmd+9byQPjMV0VEREREpCmdc79niVP1WXORMZ72Rrjiiit4/fXXycrKYvr06bz44otkZ2ezatUqIiMjGThwIMXFxY0Mvv78KWLStp1zv+cBqC4AD4SIiIiIiARA0lUwZa63Wrx5fk+Z2+h6FdOnT+eVV17h9ddf54orriA/P58ePXoQGRnJxx9/zK5duwITfz1pBK4uVf/DL33QM22yc19P8qYCJiIiIiIizUPSVQH/fj5q1CgKCwtJSEigd+/eXHPNNUyZMoXk5GTGjh3L8OHDA/p+/lIC548gPBAiIiIiItK8rV9/pPplt27d+Pzzz31ed+DAgaYKSVMoRUREREREWgolcCIiIiIiIi2EEjgREREREWl2QrHHWijU93MqgRMRERERkWYlOjqa3NzcVp/EOefIzc0lOjra7z4qYiIiIiIiIs1K3759SUtLIzs7O9ShBF10dDR9+/q/x7QSOBERERERaVYiIyNJTEwMdRjNkqZQioiIiIiItBBK4ERERERERFoIJXAiIiIiIiIthDXHyi5mlg3sCnUcPnQDckIdhLRaer4kmPR8STDp+ZJg0vMlwdZcn7EBzrnuNRubZQLXXJnZSudccqjjkNZJz5cEk54vCSY9XxJMer4k2FraM6YplCIiIiIiIi2EEjgREREREZEWQglc/cwLdQDSqun5kmDS8yXBpOdLgknPlwRbi3rGtAZORERERESkhdAInIiIiIiISAuhBE5ERERERKSFUALnBzObbGapZrbNzGaFOh5p+czsWTPba2YbqrV1NbMlZrbV+7tLKGOUlsnM+pnZx2a2ycw2mtnt3nY9XxIQZhZtZl+Z2VrvM/Y7b7ueMQkIMws3s9Vm9m/vsZ4tCRgz22lm681sjZmt9La1qGdMCVwdzCwceBK4EBgJzDCzkaGNSlqB54DJNdpmAUudc0OApd5jkfoqB+50zo0ATgFu9v5/lp4vCZQS4Gzn3BhgLDDZzE5Bz5gEzu3ApmrHerYk0M5yzo2ttvdbi3rGlMDVbSKwzTm3wzlXCrwCTA1xTNLCOeeWAftqNE8Fnve+fh6Y1pQxSevgnMt0zn3tfV2I50tQAnq+JECcxwHvYaT3x6FnTALAzPoCFwPPVGvWsyXB1qKeMSVwdUsAdlc7TvO2iQRaT+dcJni+hAM9QhyPtHBmNhAYB3yJni8JIO8UtzXAXmCJc07PmATKo8AvgcpqbXq2JJAcsNjMVpnZTG9bi3rGIkIdQAtgPtq094KINGtm1gF4A/i5c67AzNf/lYk0jHOuAhhrZnHAAjMbHeKQpBUws0uAvc65VWZ2ZojDkdZrknMuw8x6AEvMbHOoA6ovjcDVLQ3oV+24L5ARolikddtjZr0BvL/3hjgeaaHMLBJP8vaic+5Nb7OeLwk451we8AmeNb16xqSxJgGXmtlOPEtWzjazF9CzJQHknMvw/t4LLMCzXKpFPWNK4Oq2AhhiZolmFgVMB94JcUzSOr0D/Mj7+kfA2yGMRVoo8wy1/RPY5Jz732qn9HxJQJhZd+/IG2YWA5wLbEbPmDSSc+5e51xf59xAPN+3PnLO/QA9WxIgZtbezDpWvQbOBzbQwp4xc06zAetiZhfhmZMdDjzrnPtDaCOSls7MXgbOBLoBe4AHgLeA+UB/4FvgSudczUInIsdlZqcB/wHWc2QNya/wrIPT8yWNZmZJeBb5h+P5Q/B859yDZhaPnjEJEO8Uyrucc5fo2ZJAMbMT8Iy6gWcp2UvOuT+0tGdMCZyIiIiIiEgLoSmUIiIiIiIiLYQSOBERERERkRZCCZyIiIiIiEgLoQRORERERESkhVACJyIiIiIi0kIogRMRkVbLzCrMbE21n1kBvPdAM9sQqPuJiIj4IyLUAYiIiARRkXNubKiDEBERCRSNwImISJtjZjvN7E9m9pX3Z7C3fYCZLTWzdd7f/b3tPc1sgZmt9f6c6r1VuJn9w8w2mtliM4sJ2YcSEZE2QQmciIi0ZjE1plBeXe1cgXNuIvAE8Ki37Qng/znnkoAXgbne9rnAp865McB4YKO3fQjwpHNuFJAHXB7UTyMiIm2eOedCHYOIiEhQmNkB51wHH+07gbOdczvMLBLIcs7Fm1kO0Ns5V+Ztz3TOdTOzbKCvc66k2j0GAkucc0O8x/cAkc65h5rgo4mISBulETgREWmrXC2va7vGl5JqryvQ2nIREQkyJXAiItJWXV3t9+fe18uB6d7X1wCfeV8vBX4GYGbhZtapqYIUERGpTn8pFBGR1izGzNZUO/7AOVe1lUA7M/sSzx8zZ3jbbgOeNbO7gWzgOm/77cA8M7sez0jbz4DMYAcvIiJSk9bAiYhIm+NdA5fsnMsJdSwiIiL1oSmUIiIiIiIiLYRG4ERERERERFoIjcCJiIiIiIi0EErgREREREREWgglcCIiIiIiIi2EEjgREREREZEWQgmciIiIiIhIC/H/AUgDvos9KoXFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = None\n",
    "trainer = None\n",
    "\n",
    "D,C,std,r= 3*32*32,10,1e-2,0.6\n",
    "model = FullyConnectedNet(input_dim=D, hidden_dims=[100,50], num_classes=C, weight_scale=std, dropout=0.7)\n",
    "trainer = Trainer(model,data,update_rule='sgd',\n",
    "                updater_config={'learning_rate': 1e-3,},\n",
    "                lr_decay=0.95,num_epochs=50, batch_size=500,print_every=300)\n",
    "trainer.train()\n",
    "# 可视化训练/验证结果\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.title('Training loss')\n",
    "plt.plot(trainer.loss_history, 'o')\n",
    "plt.xlabel('Iteration')\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.title('Accuracy')\n",
    "plt.plot(trainer.train_acc_history, '-o', label='train')\n",
    "plt.plot(trainer.val_acc_history, '-o', label='val')\n",
    "plt.plot([0.5] * len(trainer.val_acc_history), 'k--')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(15, 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "13610e1d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
